Past Events

  • This workshop will cover the ins & outs of using REDCap for electronically consenting research participants and provide a guided demonstration of the e-Consent template. Participants will learn how to design and customize e-Consent surveys and learn best practices and common pitfalls to avoid.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Geared toward novice REDCap users, this class answers what REDCap is, why you want to use it, and goes through the entire lifecycle of a REDCap project – from initial setup to data entry and exporting your data.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Rough path theory emerged as a branch of stochastic analysis to give an improved approach to dealing with the interactions of complex random systems. In that context, it continues to resolve important questions, but its broader theoretical footprint has been substantial. Most notable is its contribution to Hairer’s Fields-Medal-winning work on regularity structures. At the core of rough path theory is the so-called signature transform which, while being simple to define, has rich mathematical properties bringing in aspects of analysis, geometry, and algebra. Hambly and Lyons (Annals of Math, 2010) built upon earlier work of Chen, showing how the signature represents the path uniquely up to generalized reparameterizations. This turns out to have practical implications allowing one to summarise the space of functions on unparameterized paths and data streams in a very economical way.

    Over the past five years, a significant strand of applied work has been undertaken to exploit the mathematical richness of this object in diverse data science challenges from healthcare, to computer vision to gesture recognition. The log signature is becoming a powerful way to summarise the fine structure of a data stream in a neural net. The emergence of neural differential equations as an important tool in data science further deepens the connections with rough paths.

    This four-day workshop will bring together key expertise across disciplines to advance understanding on some of the most pressing and exciting challenges. The week will begin with five structured, tutorial-style lectures on the foundational aspects of signatures their use in data science and topics of broad appeal such as Neural Rough Differential Equations. During the rest of the week, the morning activities will be broadly based with the afternoons focusing on more technical talks with additional opportunities for small group/breakout sessions

    Mathematics, Art, STEM, Research, Mathematics, Technology, Engineering
  • This workshop will cover the ins & outs of using REDCap for electronically consenting research participants and provide a guided demonstration of the e-Consent template. Participants will learn how to design and customize e-Consent surveys and learn best practices and common pitfalls to avoid.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • The Advance-CTR Translational Research Seminar Series showcases clinical and translational research from across Rhode Island. Presentations, followed by feedback, allow presenters the opportunity to refine and strengthen their research. Seminars are held virtually on the second Thursday of each month.

    June

    Details: June 10, 2021 at 12 p.m. ET.

    Biology, Medicine, Public Health, Entrepreneurship, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research, Training, Professional Development
  • Please join the Carney Institute for Brain Science for a special seminar featuring Kanaka Rajan, Ph.D., assistant professor at Icahn School of Medicine at Mount Sinai.

    Brown authentication is required.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research
  • Zoom Link - Available on 5/19 at 4:00 pm

     

    ROBERTA DE VITO

    Assistant Professor of Data Science and Biostatistics

     

    MULTI-STUDY FACTOR ANALYSIS IN GENOMIC AND EPIDEMIOLOGICAL DATA

    Biostatistics and computational biology are increasingly facing the urgent challenge of efficiently dealing with a large amount of experimental data. In particular, high-throughput assays are transforming the study of biology, as they generate a rich, complex, and diverse collection of high-dimensional data sets. Through compelling statistical analysis, these large data sets lead to discoveries, advances, and knowledge that were never accessible before, via compelling statistical analysis. Building such systematic knowledge is a cumulative process which requires analyses that integrate multiple sources, studies, and technologies. The increased availability of ensembles of studies on related clinical populations, technologies, and genomic features poses four categories of important multi-study statistical questions: 1) To what extent is biological signal reproducibly shared across different studies? 2) How can this global signal be extracted? 3) How can we detect and quantify local signals that may be masked by strong global signals? 4) How do these global and local signals manifest differently in different data types?

    We will answer these four questions by introducing a novel class of methodologies for the joint analysis of different studies. The goal is to separately identify and estimate 1) common factors reproduced across multiple studies, and 2) study-specific factors. We present different medical and biological applications. In all the cases, we clarify the benefits of a joint analysis compared to the standard methods.

    Our method could accelerate the pace at which we can combine unsupervised analysis across different studies, and understand the cross-study reproducibility of signal in multivariate data.

     

    Please note that this virtual event, including attendees’ Zoom video, audio and screen name, and questions or chats, will be recorded. All or portions of the event recording may be shared through Brown University’s digital channels. Individuals who do not want their identities to be captured are solely responsible for turning off their camera, muting their microphone, and/or adjusting their screen name accordingly. By attending this event, you consent to your name, voice, and/or image being recorded and to Brown University reproducing, distributing, and otherwise displaying the recording, within its sole discretion.
  • This month’s Translational Research Seminar “Updates About and Opportunities from the COBRE Center for Central Nervous System Function” presented by Jerome Sanes, PhD Professor of Neuroscience, Director of MRI Facility. Register now.

    The Advance-CTR Translational Research Seminar Series showcases clinical and translational research from across Rhode Island. Presentations, followed by feedback, allow presenters the opportunity to refine and strengthen their research. Seminars are held virtually on the second Thursday of each month.

    Biology, Medicine, Public Health, Entrepreneurship, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research, Training, Professional Development
  • Zoom Link - Available on 5/12 at 4:00 pm

     

    BRENDA RUBENSTEIN

    Joukowsky Family Assistant Professor of Chemistry

     

    PREDICTING VIABILITY: HOW PROTEIN FOLDING, BINDING, AND DYNAMICS CORRELATE WITH FITNESS

    Please see the abstract PDF (linked on the left).

  • What is CRISPR? How does gene editing work? 

    Join the Carney Institute for Brain Science for a conversation about the future of gene editing in neuroscience with Kate O’Connor-Giles, Provost’s Associate Professor of Brain Science at Brown University. 

    This conversation will be moderated by Diane Lipscombe, Reliance Dhirubhai Ambani Director of the Carney Institute, and Christopher Moore, associate director of the Carney Institute.

    Watch previous conversations on the Carney Institute website.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research
  •  

    Zoom Link - Available on 4/21 at 2:00 pm

     

    PIETER KLEER

    Assistant Professor, Tilburg University

     

    SECRETARY AND ONLINE MATCHING PROBLEMS WITH MACHINE LEARNED ADVICE

    We study online selection problems in which the goal is to select a set of elements arriving online that maximize a given objective function. In our setting, we are given some (machine-learned) information regarding the optimal (offline) solution to the problem. Following a recent line of work, the goal is to incorporate this information in existing (constant-factor) approximation algorithms such that:

    1. One gets an improved approximation guarantee in case the machine-learned information is accurate; and
    2. One does not lose too much in the approximation guarantee of the original algorithm in case the information is highly inaccurate.

    In this talk, I will illustrate these concepts using the classical secretary problem, and discuss an extension to online bipartite matching.

    Joint work with Antonios Antoniadis, Themis Gouleakis, and Pavel Kolev. Appeared in NeurIPS 2020.

     

    BIOGRAPHY

    Pieter Kleer is an Assistant Professor at Tilburg University (The Netherlands) since April 1, 2021. He completed his Ph.D. at CWI (The Netherlands) supervised by Guido Schäfer, and after that was a Postdoctoral Researcher at the Max Planck Institute for Informatics (Germany) hosted by Kurt Mehlhorn. His research interests include algorithmic game theory, online approximation algorithms and approximate uniform sampling of combinatorial objects. Earlier this year, he received de Gijs de Leve award for his PhD thesis, which is awarded once every three years by the Dutch OR society.

     

    Please note that this virtual event, including attendees’ Zoom video, audio and screen name, and questions or chats, will be recorded. All or portions of the event recording may be shared through Brown University’s digital channels. Individuals who do not want their identities to be captured are solely responsible for turning off their camera, muting their microphone, and/or adjusting their screen name accordingly. By attending this event, you consent to your name, voice, and/or image being recorded and to Brown University reproducing, distributing, and otherwise displaying the recording, within its sole discretion.

     

    Data Wednesdays

    The Data Science Initiative joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science.

  •  

    Zoom Link - Available on 4/14 at 4:00 pm

     

    TINA ELIASSI-RAD

    Professor of Computer Science, Northeastern University

    GEOMETRIC AND TOPOLOGICAL GRAPH ANALYSIS FOR MACHINE LEARNING APPLICATIONS

    This talk has two parts: (1) geometric analysis for graph embedding and (2) topological analysis for graph distances. First, graph embedding seeks to build an accurate low-dimensional representation of a graph. This low-dimensional representation is then used for various downstream tasks such as link prediction. One popular approach is Laplacian Eigenmaps, which constructs a graph embedding based on the spectral properties of the Laplacian matrix of a graph. The intuition behind it, and many other embedding techniques, is that the embedding of a graph must respect node similarity: similar nodes must have embeddings that are close to one another. We dispose of this distance-minimization assumption. In its place, we use the Laplacian matrix to find an embedding with geometric properties (instead of spectral ones) by leveraging the simplex geometry of the graph. We introduce Geometric Laplacian Eigenmap Embedding (or GLEE for short) and demonstrate that it outperforms various other techniques (including Laplacian Eigenmaps) in the tasks of graph reconstruction and link prediction. This work is joint with Leo Torres and Kevin Chan, and was published in the Journal of Complex Networks in March 2020 (http://eliassi.org/papers/torres_jcn2020.pdf). Second, measuring graph distance is a fundamental task in graph mining. For graph distance, determining the structural dissimilarity between networks is an ill-defined problem, as there is no canonical way to compare two networks. Indeed, many of the existing approaches for network comparison differ in their heuristics, efficiency, interpretability, and theoretical soundness. Thus, having a notion of distance that is built on theoretically robust first principles and that is interpretable with respect to features ubiquitous in complex networks would allow for a meaningful comparison between different networks. We rely on the theory of the length spectrum function from algebraic topology, and its relationship to the non-backtracking cycles of a graph, in order to introduce the Non-Backtracking Spectral Distance (NBD) for measuring the distance between undirected, unweighted graphs. NBD is interpretable in terms of features of complex networks such as presence of hubs and triangles. We showcase the ability of NBD to discriminate between networks in both real and synthetic data sets. This work is joint with Leo Torres and Pablo Suarez-Serrato and was published in the Journal of Applied Network Science in June 2019 (http://eliassi.org/papers/appliednetsci19_nbd.pdf).

    BIOGRAPHY

    Tina Eliassi-Rad is a Professor of Computer Science at Northeastern University in Boston, MA. She is also a core faculty member at Northeastern’s Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that, she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research is at the intersection of data mining, machine learning, and network science. She has over 100 peer-reviewed publications (including a few best papers and best paper runner-up awardees), and has given over 200 invited talks and 14 tutorials. Tina’s work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, and ethics in machine learning. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2017, Tina served as the program co-chair for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (a.k.a. KDD, which is the premier conference on data mining) and as the program co-chair for the International Conference on Network Science (a.k.a. NetSci, which is the premier conference on network science). In 2020, she served as the program co-chair for the International Conference on Computational Social Science (a.k.a. IC2S2, which is the premier conference on computational social science). Tina received an Outstanding Mentor Award from the Office of Science at the US Department of Energy in 2010; became a Fellow of the ISI Foundation in Turin Italy in 2019, and was named one of the 100 Brilliant Women in AI Ethics for 2021. She joined the Inaugural External Faculty at the Vermont Complex Systems Center in 2021.

     

    Please note that this virtual event, including attendees’ Zoom video, audio and screen name, and questions or chats, will be recorded. All or portions of the event recording may be shared through Brown University’s digital channels. Individuals who do not want their identities to be captured are solely responsible for turning off their camera, muting their microphone, and/or adjusting their screen name accordingly. By attending this event, you consent to your name, voice, and/or image being recorded and to Brown University reproducing, distributing, and otherwise displaying the recording, within its sole discretion.

     

    Data Wednesdays

    The Data Science Initiative joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science.

  • The Advance-CTR Translational Research Seminar Series showcases clinical and translational research from across Rhode Island. Presentations, followed by feedback, allow presenters the opportunity to refine and strengthen their research. Seminars are held virtually on the second Thursday of each month.

    April

    Details: April 8, 2021 at 12 p.m. ET.

    Biology, Medicine, Public Health, Entrepreneurship, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research, Teaching & Learning, Training, Professional Development
  •  

    MATTHEW HAHN

    Distinguished Professor, Departments of Biology and Computer Science,

    Director, Center for Genomics and Bioinformatics, University of Indiana

     

    THE EVOLUTION OF MAMMALIAN MUTATION RATES

     

    BIOGRAPHY

    Matthew W. Hahn earned his B.S. degree from Cornell University and obtained his Ph.D. from Duke University under the mentorship of Mark Rausher. He was an NSF postdoctoral fellow at the University of California, Davis with Charles Langley and John Gillespie. He is a Distinguished Professor of Biology and Computer Science at Indiana University, where he has held a faculty position since 2005. He currently directs IU’s Center for Genomics and Bioinformatics.

    His research focuses on how evolution has shaped organismal and genomic diversity, and how adaptation is achieved using multiple different types of molecular changes. His lab couples empirical studies of genome sequences with the development of mathematical theory, new statistical models, and the implementation of open-source software. His work introduced the first methods for quantifying and understanding gene gain and loss among species, applying them to primates to understand the differences between humans and our closest relatives. More recent methods developed by his lab have uncovered both the genes and the traits shared between species due to hybridization.

    He has published over 150 scientific articles and two books, which have collectively been cited more than 22,000 times. His research program has been supported by the U.S. NSF and NIH, as well as by the Australian Research Council. He has been the recipient of an NSF CAREER Award, a fellowship from the Alfred P. Sloan Foundation, the Margaret Dayhoff Award from the Society for Molecular Biology and Evolution, the Stebbins Medal from the International Association for Plant Taxonomy, and the Bicentennial Medal from Indiana University. He is an elected fellow of the American Association for the Advancement of Science.

     

    Please note that this virtual event, including attendees’ Zoom video, audio and screen name, and questions or chats, will be recorded. All or portions of the event recording may be shared through Brown University’s digital channels. Individuals who do not want their identities to be captured are solely responsible for turning off their camera, muting their microphone, and/or adjusting their screen name accordingly. By attending this event, you consent to your name, voice, and/or image being recorded and to Brown University reproducing, distributing, and otherwise displaying the recording, within its sole discretion.

     

    Data Wednesdays

    The Data Science Initiative joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science.

  •  

     

    DEEPS COLLOQUIUM

     

    Jnaneshwar Das

    Assistant Research Professor, Arizona State University

    Closing the loop on semantic mapping for ecological and geological sciences, leveraging robotics and AI

    My talk will present our methodology for closing the loop on data collection, analysis, and experimental design, with applications in the ecological and geological sciences. I will illustrate how we are leveraging robotics and artificial intelligence, with domain experts in the loop, to iteratively improve our data-driven models, and robot mission planning to optimally collect the data to learn
    these models. First, I will present a drone-based data-driven geomorphology pipeline for mapping rocky fault scarps, and sparse geologic features such as precariously balanced rocks. Then, I will discuss how we are able to translate aspects of this research to the task of mapping hypolith habitats in imagery from drylands, and leaving earth for a short detour, even craters from martian and lunar
    orbital imagery. Finally, I will describe our efforts in the aquatic realm, with robotic boats and underwater drones, for littoral monitoring and coral reef mapping. I will close with highlights from our annual NSF-funded annual cyber-physical systems competition where students are challenged to develop autonomy for drones, to enable deployment and recovery of sensor probes. Plans for the 2021 event
    will be showcased.

  • Neeraja Yadwadkar
    Stanford University
    Please Note: Brown Login Required For This Talk
    Abstract: Today, even after more than a decade of the cloud computing revolution, users still do not have predictable performance for their applications, and the providers continue to suffer loss of revenue due to poorly utilized resources. Moreover, the environmental implications of these inefficiencies are dire: Cloud-hosted datacenters consume as much power as a city of a million people and emit roughly as much CO2 as the airline industry. Fighting these implications, especially in the post Moore’s law era, is crucial.

     

    My work points out that the root of these inefficiencies is the gap between the users and the providers. To overcome this divide, my research brings out two key insights for building systems that render the cloud smart, a cloud that is easy-to-use, adaptive, and efficient. First, we must design interfaces to these systems that are intuitive and expressive for users. Such interfaces should open a dialog between users and providers, allowing users to specify high-level application goals, and transfer the responsibility of making low-level resource management decisions to the providers. This opens an opportunity for providers to optimize the use of their resources while still best aligning with user goals. Second, to make the resource management decisions in an adaptive manner in increasingly complex cloud systems, we must leverage Data-Driven or Machine Learning (ML) models. In doing so, my work uses and develops ML algorithms and studies the challenges that such data-driven models raise in the context of systems: modeling uncertainty, cost of training, and generalizability. In this talk, I will present two systems, INFaaS and PARIS, designed to demonstrate the efficacy of these two key insights. These systems represent key steps towards building a smart cloud: they significantly simplify the use of cloud, improve resource efficiency while meeting user goals.

     
    Bio: Neeraja Yadwadkar is a post-doctoral research fellow in the Computer Science Department at Stanford University, working with Christos Kozyrakis. She is a Cloud Computing Systems researcher, with a strong background in Machine Learning (ML). Neeraja’s research focuses on using and developing ML techniques for systems, and building systems for ML. Neeraja graduated with a PhD in Computer Science from the RISE Lab at University of California, Berkeley, where she was advised by Randy Katz and Joseph Gonzalez. Before starting her PhD, she received her masters in Computer Science from the Indian Institute of Science, Bangalore, India, and her bachelors from the Government College of Engineering, Pune.
    Host: Malte Schwarzkopf
  •  

    MADZA FARIAS-VIRGENS

    Ph.D. Candidate, Molecular Celluar and Integrative Physiology, UCLA

    Visiting with the Huerta-Sanchez Lab, CCMB, Brown University

     

    NEUROGENETIC AND EVOLUTIONARY PROCESSES IN BENGALESE FINCH SONG: PARALLELS AND IMPLICATIONS FOR THE STUDY OF HUMAN SPEECH

    The ability to learn how to produce sounds, in addition to associating innate sounds with external events or objects, enables speech and language acquisition in humans. Despite being quite rare or rudimentary among mammals, vocal production learning is very prominent in three bird groups: songbirds, parrots, and hummingbirds. In this work, we identify genes and biological pathways of importance for functional aspects of vocal production learning in the Bengalese finch (Lonchura striata domestica), a domesticated songbird commonly found in pet shops, but also a popular animal model in the study of learned vocal behaviors. The Bengalese finch has a remarkably complex song, in which transitions between vocal units are not fixed, introducing variability in song sequencing. This vocal complexity evolved during its domestication from the white-backed munia, a wild songbird easily found throughout East Asia. We use whole-genome sequencing data and analytical tools from population genomics to assess the contributions of selection processes (such as female choice for more complex songs) and demographic events (such as the major population bottleneck during domestication) in shaping the Bengalese finch’s genetic variation. Using genome-wide Fst scans, we identify several differentiated genomic regions between domesticated and wild songbirds, with the sex chromosome Z showing the greater proportion of highly differentiated genes. We also find that, as many domesticated animals, Bengalese finches are overall less genetically diverse than their wild ancestors, as shown by reduced average heterozygosity per sampled individual. However, genome-wide Tajimas’D scans show that genetic diversity in munias deviates less from expected across the genome, while diversity deviates more from the expected in Bengalese finches, with long stretches of the genome showing either considerable loss or gain of variability. Interestingly, domesticated and wild songbirds differ in multiple components of the dopamine system, a biopathway fundamental to vocal learning. Our results serve to guide further comparative efforts toward identifying convergent patterns of evolutionary change leading to vocal learning in our own species.

     

    BIOGRAPHY

    Madza is conducting a collaborative project between the Okanoya lab at RIKEN Brain Science Institute/University of Tokyo, the Huerta-Sanchez lab at Brown University, and the Xiao lab at IBP-UCLA. Her research investigates the evolutionary forces and neurogenetic mechanisms underlying changes in vocal behavior between Bengalese finches and their ancestral species, white-backed munias.

     

    Please note that this virtual event, including attendees’ Zoom video, audio and screen name, and questions or chats, will be recorded. All or portions of the event recording may be shared through Brown University’s digital channels. Individuals who do not want their identities to be captured are solely responsible for turning off their camera, muting their microphone, and/or adjusting their screen name accordingly. By attending this event, you consent to your name, voice, and/or image being recorded and to Brown University reproducing, distributing, and otherwise displaying the recording, within its sole discretion.

     

    Data Wednesdays

    The Data Science Initiative joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science.

  • Kexin Rong
    Stanford University
    Please Note: Brown Login Required For This Talk
    Abstract: Data volumes are growing exponentially, fueled by an increased number of automated processes such as sensors and devices. Meanwhile, the computational power available for processing this data – as well as analysts’ ability to interpret it – remain limited. As a result, database systems must evolve to address these new bottlenecks in analytics. In my work, I ask: how can we adapt classic ideas from database query processing to modern compute- and analyst-limited data analytics?
    In this talk, I will discuss the potential for this kind of systems development through the lens of several practical systems I have developed. By drawing insights from database query optimization, such as pushing workload- and domain-specific filtering, aggregation, and sampling into core analytics workflows, we can dramatically improve the efficiency of analytics at scale. I will illustrate these ideas by focusing on two systems — one designed to optimize visualizations for streaming infrastructure and application telemetry and one designed for high-volume seismic waveform analysis — both of which have been field-tested at scale. I will also discuss lessons from production deployments at companies including Datadog, Microsoft, Google and Facebook.
    Bio: Kexin Rong is a Ph.D. student in Computer Science at Stanford University, co-advised by Professor Peter Bailis and Professor Philip Levis. She designs and builds systems to enable data analytics at scale, supporting applications including scientific analysis, infrastructure monitoring, and analytical queries on big data clusters. Prior to Stanford, she received her bachelor’s degree in Computer Science from California Institute of Technology.

    Host: Stephen Bach
  • The Technology and Structural Inequality speaker series will focus on the impact of technology on marginalized communities. The series will bring together leading academics and activists whose work is influencing how we think about and how we fight against the harms that technology is causing. The speakers will examine how AI and machine learning algorithms can be biased and discriminatory.

    Please join us for a roundtable discussion on bias and discrimination in AI on March 31, 2021 at 11 a.m. This discussion will feature:

    • Rediet Abebe, Assistant Professor of Computer Science at the University of California, Berkeley and a Junior Fellow at the Harvard Society of Fellows
    • Mutale Nkonde, founding CEO of AI For the People (AFP), Practitioner Fellow at the Digital Civil Society Lab at Stanford, and an affiliate at the Berkman Klein Center of Internet and Society at Harvard University
    • Meredith Broussard, Associate Professor of Journalism, Arthur L. Carter Journalism Institute, New York University

    Moderated by Seny Kamara, Associate Professor of Computer Science at Brown University and Chief Scientist at Aroki Systems.

    Free and open to the public. Please register to attend.

    Presented by the Center for the Study of Race and Ethnicity in America (CSREA) in partnership with the Department of Computer Science’s Computing for the People Project.

    Speaker Bios

    Rediet Abebe is an assistant professor of computer science at the University of California, Berkeley and a Junior Fellow at the Harvard Society of Fellows. Abebe holds a Ph.D. in computer science from Cornell University and graduate degrees in mathematics from Harvard University and the University of Cambridge. Her research is in artificial intelligence and algorithms, with a focus on equity and justice concerns. Abebe co-founded and co-organizes Mechanism Design for Social Good (MD4SG) – a multi-institutional, interdisciplinary initiative. Her dissertation received the 2020 ACM SIGKDD Dissertation Award and an honorable mention for the ACM SIGEcom Dissertation Award for offering the foundations of this emerging research area. Abebe’s work has informed policy and practice at the National Institute of Health (NIH) and the Ethiopian Ministry of Education. She has been honored in the MIT Technology Reviews’ 35 Innovators Under 35 and the Bloomberg 50 list as a one to watch. Abebe also co-founded Black in AI, a non-profit organization tackling equity issues in AI. Her research is influenced by her upbringing in her hometown of Addis Ababa, Ethiopia.

    Mutale Nkonde is the founder of AI For the People (AFP), a nonprofit communications firm.. AFP’s mission is to produce content that empowers general audiences to combat racial bias in tech. Prior to starting AI for the People, Nkonde worked in AI Governance. During that time, she was part of the team that introduced the Algorithmic Accountability Act, the DEEP FAKES Accountability Act, and the No Biometric Barriers to Housing Act to the US House of Representatives. In 2021 Nkonde was the lead author of Disinformation Creep: ADOS and the Weaponization of Breaking News, Harvard Kennedy School Misinformation Review, which kicked off her work in mis and disinformation. AI for the People recently co-produced a film with Amnesty International to support the ban the scan campaign a global push to ban facial recognition.

    Meredith Broussard is an associate professor at the Arthur L. Carter Journalism Institute of New York University and the author of the award-winning book Artificial Unintelligence: How Computers Misunderstand the World. Her research focuses on artificial intelligence in investigative reporting, with a particular interest in using data analysis for social good. She is an affiliate faculty member at the Moore Sloan Data Science Environment at the NYU Center for Data Science, a 2019 Reynolds Journalism Institute Fellow, and her work has been supported by the Institute of Museum & Library Services as well as the Tow Center at Columbia Journalism School. A former features editor at the Philadelphia Inquirer, she has also worked as a software developer at AT&T Bell Labs and the MIT Media Lab. Her features and essays have appeared in The Atlantic, Slate, Vox, and other outlets. Follow her on Twitter @merbroussard or contact her via meredithbroussard.com.

    Identity, Culture, Inclusion, Mathematics, Technology, Engineering
  • Maria Apostolaki
    ETH Zurich
    Please Note: Brown Login Required For This Talk
     
    Title: Building secure distributed systems atop the insecure Internet
     
    Abstract: Distributed systems are increasingly important for our everyday life, allowing for high performance, fault tolerance, and flexibility. Many of these systems nowadays rely on the inherently insecure Internet infrastructure. Surely though, they should have been designed to take this into account…?

    In this talk, I will answer negatively to this question using a concrete example: public blockchain systems such as Bitcoin. These are novel distributed systems that are designed according to stringent failure models. In this context, I will explain how an adversary controlling pieces of Internet infrastructure can practically compromise: (i) Bitcoin’s consensus protocol (by partitioning the network); (ii) Bitcoin’s anonymity guarantees (by mapping pseudonyms to real-world identities); and (iii) Bitcoin’s availability (by eclipsing clients).

    While these attacks are worrying, I will also introduce practical and effective defenses to counter them both at the network and the application layer. Beyond Bitcoin, this work teaches essential lessons for distributed-system design.
    Bio: Maria Apostolaki is a PhD Student at ETH Zurich advised by Laurent Vanbever.
    During her studies, she has been a visiting student at MIT (2019) and a research intern at Microsoft Research (2018) and Google (2017). Before joining ETH, she earned her diploma in Electrical and Computer Engineering at the National Technical University of Athens, Greece.
    Host: Theo Benson
  • Wei Hu
    Princeton University
    Please Note: Brown Login Required For This Talk
    Abstract: Despite the phenomenal empirical successes of deep learning in many application domains, its underlying mathematical mechanisms remain poorly understood. Mysteriously, deep neural networks in practice can often fit training data almost perfectly and generalize remarkably well to unseen test data, despite highly non-convex optimization landscapes and significant over-parameterization. A solid theory not only can help us understand such mysteries, but also will be the key to improving the practice of deep learning and making it more principled, reliable, and easy-to-use.
    In this talk, I will present our recent progress on building the theoretical foundations of deep learning, by opening the black box of the interactions among data, model architecture, and training algorithm. First, I will show that gradient descent on deep linear neural networks induces an implicit bias towards low-rank solutions, which leads to an improved method for the classical low-rank matrix completion problem. Next, turning to nonlinear deep neural networks, I will talk about a line of studies on wide neural networks, where by drawing a connection to the neural tangent kernels, we can answer various questions such as how training loss is minimized, why trained network can generalize well, and why certain component in the network architecture is useful; we also use theoretical insights to design a new simple and effective method for training on noisily labeled datasets. In closing, I will discuss key questions going forward towards building practically relevant theoretical foundations of modern machine learning.
     
    Bio: Wei Hu is a PhD candidate in the Department of Computer Science at Princeton University, advised by Sanjeev Arora. Previously, he obtained his B.E. in Computer Science from Tsinghua University. He has also spent time as a research intern at research labs of Google and Microsoft. His current research interest is broadly in the theoretical foundations of modern machine learning. In particular, his main focus is on obtaining solid theoretical understanding of deep learning, as well as using theoretical insights to design practical and principled machine learning methods. He is a recipient of the Siebel Scholarship Class of 2021.
    Host: Eli Upfal
  • Brown CS now offers a Master of Science in Cybersecurity that is fully online and open to 5th years. This degree is designed to be completed from anywhere in the world, so you can move back to your hometown or move to a new city to start a new job.

    The program offers a Computer Science Track for those interested in building technical capabilities and a Policy Track for students looking to approach cybersecurity problems from legal and legislative perspectives.

    Join directors Tim Edgar, Bernardo Palazzi, and Ernie Zaldivar for a discussion of what the program has to offer.

  • Hyunseung Kang, PhD

    Assistant Professor, Department of Statistics, University of Wisconsin-Madison

    Title:
    Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Spillover Effects Among A Vulnerable Subpopulation
    Abstract:
    Cluster randomized trials (CRTs) are a popular design to study the effect of interventions in infectious disease settings. However, standard analysis of CRTs primarily relies on strong parametric methods, usually a Normal mixed effect models to account for the clustering structure, and focus on the overall intent-to-treat (ITT) effect to evaluate effectiveness. The paper presents two methods to analyze two types of effects in CRTs, the overall and heterogeneous ITT effects and the spillover effect among never-takers who cannot or refuse to take the intervention. For the ITT effects, we make a modest extension of an existing method where we do not impose parametric models or asymptotic restrictions on cluster size. For the spillover effect among never-takers, we propose a new bound-based method that uses pre-treatment covariates, classification algorithms, and a linear program to obtain sharp bounds. A key feature of our method is that the bounds can become dramatically narrower as the classification algorithm improves and the method may also be useful for studies of partial identification with instrumental variables. We conclude by reanalyzing a CRT studying the effect of face masks and hand sanitizers on transmission of 2008 interpandemic influenza in Hong Kong. This is joint work with Chan Park (UW-Madison)
    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Sarah Dean
    UC Berkeley
    Please Note: Brown Login Required For This Talk
    Title: Reliable Machine Learning in Feedback Systems
    Abstract: Machine learning techniques have been successful for processing complex information, and thus they have the potential to play an important role in data-driven decision-making and control. However, ensuring the reliability of these methods in feedback systems remains a challenge, since classic statistical and algorithmic guarantees do not always hold.

    In this talk, I will provide rigorous guarantees of safety and discovery in dynamical settings relevant to robotics and recommendation systems. I take a perspective based on reachability, to specify which parts of the state space the system avoids (safety) or can be driven to (discovery). For data-driven control, we show finite-sample performance and safety guarantees which highlight relevant properties of the system to be controlled. For recommendation systems, we introduce a novel metric of discovery and show that it can be efficiently computed. In closing, I discuss how the reachability perspective can be used to design social-digital systems with a variety of important values in mind.
    Bio: Sarah is a PhD candidate in the Department of Electrical Engineering and Computer Science at UC Berkeley, advised by Ben Recht. She received her MS in EECS from Berkeley and BSE in Electrical Engineering and Math from the University of Pennsylvania. Sarah is interested in the interplay between optimization, machine learning, and dynamics in real-world systems. Her research focuses on developing principled data-driven methods for control and decision-making, inspired by applications in robotics, recommendation systems, and developmental economics. She is a co-founder of a transdisciplinary student group, Graduates for Engaged and Extended Scholarship in computing and Engineering, and the recipient of a Berkeley Fellowship and a NSF Graduate Research Fellowship.
    Host: Karianne Bergen
  • Please note the following talk is only available to faculty members.

     

    SEAN MONAGHAN, MD, FACS

    Assistant Professor of Surgery, Brown University

     

    Lots of Data from Really Sick Patients: What to Do?

    The generation of genomic data is exceeding the generation of data from other sources such as YouTube and Twitter. When the genomic data comes from a patient or multiple patients with similar diseases how do we utilize it to inform clinical practice? When this data comes from very sick patients, can we use it to better care? How can we manage all this data as it relates to patients when we are trained to use very specific tests. This talk will highlight some data from critically ill patients with COVID and how we use that data in patient care. We will also discuss other data management techniques and why they may not be applicable in the real-world scenario of COVID-19. It is hoped that there will be a robust discussion of other techniques from other disciplines.

     

    BIOGRAPHY

    Sean F. Monaghan, MD, FACS, is an Assistant Professor of Surgery at Brown University and a member of the Division of Trauma and Surgical Critical Care and the Division of Surgical Research. His research attempts to understand the biology of RNA splicing in critically ill trauma and surgical patients using Acute Respiratory Distress Syndrome (ARDS) as a model disease. He uses both human samples and animal models as well as large data sets (GTEx) with computation and molecular biology techniques in his research. His research hopes to translate RNA sequencing technology for use by clinicians in the intensive care unit. Dr. Monaghan has been supported by the American College of Surgeons C. James Carrico Faculty Research Fellowship and the National Institutes of Health as a Pilot Project and the Principal Investigator for Project 5 of the CardioPulmonary Vascular Biology Center of Biomedical Research Excellence.

     

    (F4F) Faculty for Faculty Research Talks

    DSI Faculty for Faculty Research Talks are an opportunity for faculty to share current data science-related research activities with other faculty colleagues in an informal environment. The talks are presented at a very general level, to stimulate discussion and interdisciplinary interchange of ideas.

    Our goal is to provide a networking venue that promotes research collaborations between faculty across all disciplines; awareness of the breadth of data science-related research at Brown; and a forum for faculty to share their expertise with one another. Participation will be limited to faculty members.

  • Akshitha Sriraman
    University of Michigan
    Please Note: Brown Login Required For This Talk
    Abstract: Current hardware and software systems were conceived at a time when we had scarce compute and memory resources, limited quantity of data and users, and easy hardware performance scaling due to Moore’s Law. These assumptions are not true today. Today, emerging web services require data centers that scale to hundreds of thousands of servers, i.e., hyperscale, to efficiently process requests from billions of users. In this new era of hyperscale computing, we can no longer afford to build each layer of the systems stack separately. Instead, we must rethink the synergy between the software and hardware worlds from the ground up.
    In this talk, I will focus on re-thinking (1) software threading and concurrency paradigms and (2) data center hardware architectures. First, I will describe μTune, my software threading framework that is aware of the overheads induced by the underlying hardware’s constraints. Then, I will discuss SoftSKU and Accelerometer—my proposals to answer the question of: How should we build data center hardware for emerging software paradigms in the post-Moore era? Finally, I will conclude by describing my ongoing and future research towards re-designing the systems stack to enable the hyperscale web services of tomorrow.
    Bio: Akshitha Sriraman is a Ph.D. candidate in Computer Science and Engineering at the University of Michigan. Her research bridges computer architecture and software systems, demonstrating the importance of that bridge in realizing efficient hyperscale web services via solutions that span the systems stack. Her systems solutions to improve hardware efficiency have been deployed in real hyperscale data centers and currently serve billions of users, saving millions of dollars and meaningfully reducing the global carbon footprint. Additionally, her hardware design proposals have influenced Intel’s Alder Lake+ CPU architectures.
    Sriraman has been recognized with a Facebook Fellowship, a Rackham Merit Ph.D. Fellowship, and was selected for the Rising Stars in EECS Workshop. Her work has been recognized with an IEEE Micro Top Picks distinction and has appeared in top architecture and systems venues like OSDI, ISCA, ASPLOS, MICRO, and HPCA.
    Host: Iris Bahar
  •  

    ALICE PAUL

    Assistant Professor of Biostatistics, Brown University

    CLUSTERING WITH ITERATED LINEAR OPTIMIZATION

    In this talk, Alice will introduce a novel method for clustering using a semidefinite programming (SDP) relaxation of the Max k-Cut problem. The approach is based on a new methodology for rounding the solution of an SDP using iterated linear optimization. She shows the vertices of the Max k-Cut SDP relaxation correspond to partitions of the data into at most k sets. She also shows the vertices are attractive fixed points of iterated linear optimization. She interprets the process of fixed-point iteration with linear optimization as repeated relaxations of the closest vertex problem. Her experiments show that using fixed-point iteration for rounding the Max k-Cut SDP relaxation leads to significantly better results when compared to randomized rounding.

     

    BIOGRAPHY

    Alice Paul is an Assistant Professor of Biostatistics at Brown University interested in algorithms, optimization, data science, and education. Her past research has focused on the design and analysis of optimization algorithms underlying bike-share systems, machine learning, revenue management, and other applications. She also enjoys thinking about how data informs these algorithms. Recent research projects with students have involved using data science to analyze the use of bike-share and scooter-share programs.

    Prior to joining Brown, Alice was an Assistant Professor of Applied Math and Computer Science at Olin College of Engineering. Before that, she was a Postdoctoral Research Associate in the Data Science Initiative at Brown University under the advisement of Professor Pedro Felzenszwalb. Alice completed her Ph.D. in the Department of Operations Research and Information Engineering at Cornell University, advised by Professor David Williamson, and she received her Bachelor of Science in Mathematics from Harvey Mudd College, where her undergraduate thesis adviser was Professor Susan Martonosi.

     

    Please note that this virtual event, including attendees’ Zoom video, audio and screen name, and questions or chats, will be recorded. All or portions of the event recording may be shared through Brown University’s digital channels. Individuals who do not want their identities to be captured are solely responsible for turning off their camera, muting their microphone, and/or adjusting their screen name accordingly. By attending this event, you consent to your name, voice, and/or image being recorded and to Brown University reproducing, distributing, and otherwise displaying the recording, within its sole discretion.

     

    Data Wednesdays

    The Data Science Initiative joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science.

  • The Technology and Structural Inequality speaker series will focus on the impact of technology on marginalized communities. The series will bring together leading academics and activists whose work is influencing how we think about and how we fight against the harms that technology is causing. The speakers will examine how technology is being used to increase the surveillance and policing of marginalized communities and how many of these technologies are inherently biased and discriminatory.

    Please join us for a roundtable discussion on policing and technology on March 17, 2021 at 12 p.m. This discussion will feature:

    • Samuel Sinyangwe, Co-founder of Campaign Zero
    • Cynthia Khoo, lawyer and Research Fellow at the Citizen Lab, Munk School of Global Affairs & Public Policy, University of Toronto
    • Cierra Robson, Doctoral Student in Sociology & Social Policy, Harvard University

    Moderated by Nicole Gonzalez Van Cleve, Associate Professor of Sociology, Brown University. 

    Free and open to the public. Please register to attend.

    Presented by the Center for the Study of Race and Ethnicity in America (CSREA) in partnership with the Department of Computer Science’s Computing for the People Project.

    Samuel Sinyangwe is a policy analyst and data scientist focused on ending racism and police violence in America. Sam has supported movement activists across the country to collect and use data as a tool for fighting police violence by building Mapping Police Violence, the nation’s most comprehensive database of killings by police. Sam also co-founded Campaign Zero, a national research and advocacy organization that partners with local organizers to enact legislation to end police violence. Previously, Sam worked at PolicyLink to support communities in building cradle-to-career systems of support for low-income families. He graduated from Stanford University in 2012.

    Cynthia Khoo is a research fellow at the Citizen Lab at the University of Toronto and a technology and human rights lawyer. She holds an LL.M. with a concentration in law and technology from the University of Ottawa, where she worked on interventions before the Supreme Court of Canada as junior counsel at the Samuelson-Glushko Canadian Internet Policy and Public Interest Clinic (CIPPIC). Her paper on platform liability for emergent systemic harm to historically marginalized groups received the inaugural Ian R. Kerr Robotnik Memorial Award for the Best Paper by an Emerging Scholar at We Robot 2020 and she is currently authoring a report on platform regulation and technology-facilitated gender-based violence for the Women’s Legal Education and Action Fund (LEAF). Cynthia is the principal lawyer at Tekhnos Law, and obtained her J.D. from the University of Victoria. Her work and expertise span across key areas of technology and human rights law and policy, including privacy and surveillance, equality and freedom from discrimination, online censorship and freedom of expression, intermediary liability, and algorithmic decision-making systems.

    Cierra Robson is the associate director of the Ida B. Wells JUST Data Lab at Princeton University where she guides research teams in partnership with community organizations to explore how data can be retooled for racial justice. Additionally, Cierra is a doctoral student in the sociology and social policy program at Harvard University where she is a Malcolm Hewitt Wiener Ph.D. Research Fellow in Poverty and Justice. Broadly, her research explores the ways in which technological advancements both reinforce and revolutionize racial inequality in the United States, particularly within the criminal justice system. She holds a B.A. in African American studies from Princeton University, where she specialized in studies of race and public policy and pursued a minor in technology and society.

    Identity, Culture, Inclusion, Mathematics, Technology, Engineering
  • Haohan Wang

    Carnegie Mellon University

    Please Note: Brown Login Required For This Talk

    Abstract: The talk will introduce a series of works centering around robust machine learning. I will start with an empirical observation that shows the misalignment between the machine learning model and the human’s perception of the data, which leads to our central hypothesis of the challenge of learning robust models.

    Following the central hypothesis, I will introduce two pieces of my recent work: one is from a data perspective: when we use data augmentation, how we can take full advantage of the augmented data, and the discussion leads to a simple generic method that can compete with different state-of-the-art robust machine learning methods over multiple tasks; the other is from a model perspective: we directly build a regularization into a vanilla vision model to counter model’s tendency in predicting through local patches of images. Further, I will also briefly introduce the concept of our statistical investigation to derive a formal bound that can connect these two perspectives, and the bound will also inspire a new method.

    Finally, I will conclude with an ongoing project that applies the discussed techniques to understand the pathology of Alzheimer’s disease, which suggests that a model boosted by these robust learning techniques is likely to understand the Alzheimer’s patients’ MRI data as a physician does.

    Bio: Haohan Wang is currently a Ph.D. candidate at LTI, Carnegie Mellon University, working with Professor Eric P. Xing. He started his career in computational biology, and his recent research interest centers around trustworthy machine learning, supported by statistical analysis and covering a spectrum of applications including computer vision, NLP, and computational biology. Haohan is recognized as the Next Generation in Biomedicine by the Broad Institute of MIT and Harvard because of his contributions in dealing with confounding factors with deep learning. His survey report On the Origin of Deep Learning is considered one of the Most Influential Data Science Research Papers in 2018 by the media from Open Data Science Conference (ODSC

    Host: Ritambhara Singh

  •  

    Ph.D. Thesis Defense
    DHANANJAY BHASKAR

    Ph.D. Candidate, BME, Brown University

    “Topological Data Analysis of Collective Motion”

     

    Wednesday, March 17

    10:00 AM EDT

  • Sudipto Banerjee, PhD
    Professor and Chair of the Department of Biostatistics
    UCLA Fielding School of Public Health

    Title: Bayesian Finite Population Survey Sampling from Spatial Process Settings

    Abstract:
    We develop a Bayesian model-based approach to finite population estimation accounting for spatial dependence. Our innovation here is a framework that achieves inference for finite population quantities in spatial process settings. A key distinction from the small area estimation setting is that we analyze finite populations referenced by their geographic coordinates (point-referenced data). Specifically, we consider a two-stage sampling design in which the primary units are geographic regions, the secondary units are point-referenced locations, and the measured values are assumed to be a partial realization of a spatial process. Traditional geostatistical models do not account for variation attributable to finite population sampling designs, which can impair inferential performance. On the other hand, design-based estimates will ignore the spatial dependence in the finite population. This motivates the introduction of geostatistical processes that will enable inference at arbitrary locations in our domain of interest. We demonstrate using simulation experiments that process-based finite population sampling models considerably improve model fit and inference over models that fail to account for spatial correlation. Furthermore, the process based models offer richer inference with spatially interpolated maps over the entire region. We reinforce these improvements and also scalable inference for spatial BIG DATA analysis with millions of locations using Nearest-Neighbor and Meshed Gaussian processes. We will demonstrate our framework with an example of groundwater Nitrate levels in the population of California Central Valley wells by offering estimates of mean Nitrate levels and their spatially interpolated maps.

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Yannai Gonczarowski
    Microsoft New England
    Please Note: Brown Login Required For This Talk
    Abstract: Traditional auctions, and more generally, traditional economic transactions, have always been of “manual scale.” For example, the number of auctions conducted even in the largest of auction houses was moderate enough (and each item—expensive enough) so that expert appraisers could dedicate time to surveying each item sold, and use their expertise to determine a starting price that is optimized to maximize the revenue of the auction house. Such manual solutions were feasible until the Internet changed our world. For instance, whenever you search for something using your favorite search engine, a split-second auction is performed among (many times a large number of) advertisers who are related to your search term in order to determine which ads will be shown to you in the results page a mere moment later. The quantity of these auctions (billions per day), together with the low worth of each (many times less than a single cent each, yet in total reaching many billions of dollars), makes any per-auction manual intervention, say, in choosing the “starting price,” completely impractical.

    This explosion in online and computerized economic activity necessitates the study and understanding of economic mechanisms and markets of unprecedented scale. How good can simple mechanisms be? How complex must optimal mechanisms be? And, most substantially, what are the precise trade-offs between simplicity and quality? In this talk, I will survey some of my results on such questions for three notions of complexity within the context of the design of high-revenue auctions: the complexity of the machine-learning of high-revenue auctions, the complexity of describing high-revenue auctions, and the communication complexity of running high-revenue auctions.

    Based also on joint works with Moshe Babaioff, Noam Nisan, and S. Matthew Weinberg.

    In the talk, I will survey results from my following three papers:
    * The Sample Complexity of Up-to-ε Multi-Dimensional Revenue Maximization, Y.A.G. and S. Matthew Weinberg, Journal of the ACM, forthcoming, 2021 (preliminary version appeared in FOCS 2018). (arXiv)
    * Bounding the Menu-Size of Approximately Optimal Auctions via Optimal-Transport Duality, Y.A.G., in Proceedings of the 50th Annual ACM Symposium on Theory of Computing (STOC), pp. 123–131, 2018. (arXiv)
    * The Menu-Size Complexity of Revenue Approximation, Moshe Babaioff, Y.A.G., and Noam Nisan, in Proceedings of the 49th Annual ACM Symposium on Theory of Computing (STOC), pp. 869–877, 2017. (arXiv)
    Bio: Yannai Gonczarowski is a postdoctoral researcher at Microsoft Research New England. His main research interests lie in the interface between the theory of computation, economic theory, and game theory—an area commonly referred to as Algorithmic Game Theory. In particular, Yannai is interested in various aspects of complexity in mechanism and market design (defined broadly from auctions to matching markets), including the interface between mechanism design and machine learning. Yannai received his PhD from the Departments of Math and CS, and the Center for the Study of Rationality, at the Hebrew University of Jerusalem, where he was advised by Sergiu Hart and Noam Nisan, as an Adams Fellow of the Israel Academy of Sciences and Humanities. Throughout most of his PhD studies, he was also a long-term research intern at Microsoft Research in Herzliya. He holds an M.Sc. in Math (summa cum laude) and a B.Sc. in Math and CS (summa cum laude, Valedictorian) from the Hebrew University. Yannai is also a professionally-trained opera singer, having acquired a bachelor’s degree and a master’s degree in Classical Singing at the Jerusalem Academy of Music and Dance. Yannai’s doctoral dissertation was recognized with several awards, including the 2018 Michael B. Maschler Prize of the Israeli Chapter of the Game Theory Society and the ACM SIGecom Doctoral Dissertation Award for 2018. For the design and implementation of the National Matching System for Gap-Year Programs in Israel, he was awarded the Best Paper Award at MATCH-UP’19 and the inaugural INFORMS AMD Michael H. Rothkopf Junior Researcher Paper Prize (first place) for 2020. Yannai is also the recipient of the inaugural ACM SIGecom Award for Best Presentation by a Student or Postdoctoral Researcher at EC’18. His first textbook, “Mathematical Logic through Python” (Gonczarowski and Nisan), which introduces a new approach to teaching the material of a basic Logic course to Computer Science students, tailored to the unique intuitions and strengths of this cohort of students, is forthcoming in Cambridge University Press.
    Host: Eli Upfal
  • Yannai Gonczarowski
    Microsoft New England
    Please Note: Brown Login Required For This Talk
    Abstract: Traditional auctions, and more generally, traditional economic transactions, have always been of “manual scale.” For example, the number of auctions conducted even in the largest of auction houses was moderate enough (and each item—expensive enough) so that expert appraisers could dedicate time to surveying each item sold, and use their expertise to determine a starting price that is optimized to maximize the revenue of the auction house. Such manual solutions were feasible until the Internet changed our world. For instance, whenever you search for something using your favorite search engine, a split-second auction is performed among (many times a large number of) advertisers who are related to your search term in order to determine which ads will be shown to you in the results page a mere moment later. The quantity of these auctions (billions per day), together with the low worth of each (many times less than a single cent each, yet in total reaching many billions of dollars), makes any per-auction manual intervention, say, in choosing the “starting price,” completely impractical.

    This explosion in online and computerized economic activity necessitates the study and understanding of economic mechanisms and markets of unprecedented scale. How good can simple mechanisms be? How complex must optimal mechanisms be? And, most substantially, what are the precise trade-offs between simplicity and quality? In this talk, I will survey some of my results on such questions for three notions of complexity within the context of the design of high-revenue auctions: the complexity of the machine-learning of high-revenue auctions, the complexity of describing high-revenue auctions, and the communication complexity of running high-revenue auctions.

    Based also on joint works with Moshe Babaioff, Noam Nisan, and S. Matthew Weinberg.

    In the talk, I will survey results from my following three papers:
    * The Sample Complexity of Up-to-ε Multi-Dimensional Revenue Maximization, Y.A.G. and S. Matthew Weinberg, Journal of the ACM, forthcoming, 2021 (preliminary version appeared in FOCS 2018). (arXiv)
    * Bounding the Menu-Size of Approximately Optimal Auctions via Optimal-Transport Duality, Y.A.G., in Proceedings of the 50th Annual ACM Symposium on Theory of Computing (STOC), pp. 123–131, 2018. (arXiv)
    * The Menu-Size Complexity of Revenue Approximation, Moshe Babaioff, Y.A.G., and Noam Nisan, in Proceedings of the 49th Annual ACM Symposium on Theory of Computing (STOC), pp. 869–877, 2017. (arXiv)
    Bio: Yannai Gonczarowski is a postdoctoral researcher at Microsoft Research New England. His main research interests lie in the interface between the theory of computation, economic theory, and game theory—an area commonly referred to as Algorithmic Game Theory. In particular, Yannai is interested in various aspects of complexity in mechanism and market design (defined broadly from auctions to matching markets), including the interface between mechanism design and machine learning. Yannai received his PhD from the Departments of Math and CS, and the Center for the Study of Rationality, at the Hebrew University of Jerusalem, where he was advised by Sergiu Hart and Noam Nisan, as an Adams Fellow of the Israel Academy of Sciences and Humanities. Throughout most of his PhD studies, he was also a long-term research intern at Microsoft Research in Herzliya. He holds an M.Sc. in Math (summa cum laude) and a B.Sc. in Math and CS (summa cum laude, Valedictorian) from the Hebrew University. Yannai is also a professionally-trained opera singer, having acquired a bachelor’s degree and a master’s degree in Classical Singing at the Jerusalem Academy of Music and Dance. Yannai’s doctoral dissertation was recognized with several awards, including the 2018 Michael B. Maschler Prize of the Israeli Chapter of the Game Theory Society and the ACM SIGecom Doctoral Dissertation Award for 2018. For the design and implementation of the National Matching System for Gap-Year Programs in Israel, he was awarded the Best Paper Award at MATCH-UP’19 and the inaugural INFORMS AMD Michael H. Rothkopf Junior Researcher Paper Prize (first place) for 2020. Yannai is also the recipient of the inaugural ACM SIGecom Award for Best Presentation by a Student or Postdoctoral Researcher at EC’18. His first textbook, “Mathematical Logic through Python” (Gonczarowski and Nisan), which introduces a new approach to teaching the material of a basic Logic course to Computer Science students, tailored to the unique intuitions and strengths of this cohort of students, is forthcoming in Cambridge University Press.
    Host: Eli Upfal
  • Kalesha Bullard

    Facebook AI Research

    Please Note: Brown Login Required For This Talk

    Abstract: Effective communication is an important skill for enabling information exchange and cooperation in multi-agent systems, in which agents coexist in shared environments with humans and/or other artificial agents. Indeed, human domain experts can be a highly informative source of instructive guidance and feedback (supervision). My prior work explores this type of interaction in depth, as a mechanism for enabling learning for artificial agents. However, dependence upon human partners for acquiring or adapting skills has important limitations. Human time and cognitive load is typically constrained (particularly in realistic settings) and data collection from humans, though potentially qualitatively rich, can be slow and costly to acquire. Yet, the ability to learn through interaction with other agents represents another powerful mechanism for enabling interactive learning. Though other artificial agents may also be novices, agents can co-learn through providing each other evaluative feedback (reinforcement), given the learning task has been sufficiently structured and allows for generalization to novel settings.

    This talk presents research that investigates methods for enabling agents to learn general communication skills through interactions with other agents. In particular, the talk will focus on my ongoing work within Multi-Agent Reinforcement Learning, investigating emergent communication protocols, inspired by communication in more realistic settings. We present a novel problem setting and a general approach that allows for zero-shot coordination (ZSC), i.e., discovering protocols that can generalize to independently trained agents. We also explore and analyze specific difficulties associated with finding globally optimal ZSC protocols, as complexity of the communication task increases or the modality for communication changes (e.g. from symbolic communication to implicit communication through physical movement, by an embodied artificial agent). Overall, this work opens up exciting avenues for learning general communication protocols in complex domains.

    Bio: Kalesha Bullard is a postdoctoral researcher at Facebook AI Research. She completed her PhD in Computer Science at Georgia Institute of Technology in 2019, where her research focused within the space of interactive robot learning. During her postdoc, Kalesha has expanded her research to explore the space of multi-agent reinforcement learning, currently investigating how to enable embodied multi-agent populations to learn general communication protocols. More broadly, Kalesha’s research interests span autonomous reasoning and decision making for artificial agents in multi-agent settings. To date, her research has focused on principled methods for enabling agents to learn through interaction with other agents (human or artificial) to achieve shared goals. Beyond research, Kalesha has participated in a number of service roles throughout her research career, recently serving on organizing and program committees for workshops associated with several top Artificial Intelligence conference venues (NeurIPS, AAAI, AAMAS). This past year, she was selected as one of the 2020 Electrical Engineering and Computer Science (EECS) Rising Stars.

    Host: George Konidaris

  • Data can be an important part of a humanities research project. It can provide new insights into your work, offering new ways to understand and make sense of your subject matter. Digital humanities can be an important part of the 21st century Ph.D.’s professional skill set.

    Working with data means you need to be concerned with that data at every stage of the project. Where does it come from? How accessible is it? How has it been cleaned? How will it be preserved? How do you negotiate the choices that need to be made as you move from research questions to argument and presentation?

    Join us for a workshop on the choices you need to make when you decide to use data as part of a scholarly project. CDS staff and their graduate student partners will discuss their digital projects. How were they designed and produced? What decisions did they make about tools and process? How did that shape the project and the results? What did they learn, what choices did they make, and what would they do differently next time?

    Featured speakers and projects include, among others:

    • Talya Housman (Ph.D., History, 2019), on her dissertation “‘To Plunder All Under the Petty-Coate’: Prosecuting Sexual Crime and Gendered Violence in The English Revolution” and the database she created.
    • George Elliott (Ph.D. Candidate, History), gathering data about 17th-century Connecticut alchemist Gershom Bulkeley.
    • Maiah Letsch (Graduate Student, History, Utrecht University), on her work with the Database of Indigenous Slavery in the Americas.

    This workshop is aimed at humanities scholars and students without a background in digital scholarship. Many of these issues are also of concern for projects in the sciences and social sciences, and all are welcome.

    Presented by the Center for Digital Scholarship, the Cogut Institute for the Humanities as part of its 21st-Century Ph.D. Series, and the Data Science Initiative.

    Humanities
  • The Advance-CTR Translational Research Seminar Series showcases clinical and translational research from across Rhode Island. Presentations, followed by feedback, allow presenters the opportunity to refine and strengthen their research. Seminars are held virtually on the second Thursday of each month.

    March

    Details: March 11, 2021 at 12 p.m. ET.

    Biology, Medicine, Public Health, Entrepreneurship, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research, Teaching & Learning, Training, Professional Development
  •  

    SUMAN BERA

    Postdoctoral Researcher, UC Santa Cruz

    NEAR-LINEAR TIME HOMOMORPHISM COUNTING IN BOUNDING DEGENERACY GRAPHS: THE BARRIER OF LONG INDUCED CYCLES

    Counting homomorphisms of a constant-sized pattern graph H in an input graph G is a fundamental computational problem. There is a rich history of studying the complexity of this problem, under various constraints on the input G and the pattern H. Given the significance of this problem and the large sizes of modern inputs, we investigate when linear-time algorithms are possible. We focus on the case when the input graph has bounded degeneracy, a commonly studied and practically relevant class for homomorphism counting. We study the following question. Can we precisely characterize the patterns H for which linear-time algorithms are possible?

    We completely resolve this problem, discovering a clean dichotomy using fine-grained complexity. We prove the following: if the largest induced cycle in H has length at most 5, then there is a linear-time algorithm for counting H-homomorphisms in bounded degeneracy graphs. If the largest induced cycle in H has a length of at least 6, then (assuming standard fine-grained complexity conjectures), there is no linear time algorithm for counting H-homomorphisms.

    This is joint work with Noujan Pashanasangi and Prof. C. Seshadhri and is based on the following two papers:

    • Near-Linear Time Homomorphism Counting in Bounded Degeneracy Graphs: The Barrier of Long Induced Cycles. (SODA 2021)
    • Linear Time Subgraph Counting, Graph Degeneracy, and the Chasm at Size Six. (ITCS 2020)

     

    BIOGRAPHY

    Suman Bera is a postdoctoral researcher at UC Santa Cruz, working with Prof. C. Seshadhri. He obtained my Ph.D. in Computer Science from Dartmouth College, where he was advised by Prof. Amit Chakrabarti, and completed his masters at the Indian Institute of Technology Delhi (IIT Delhi) under the supervision of Prof. Amit Kumar. Before that, he was an undergraduate student at Jadavpur University. Somewhere in between, he spent a couple of years at IBM Research Lab (New Delhi) and Adobe India.

    Suman’s research is broadly on the topic of the foundations of data science. In particular, he is interested in large graph analysis. His work lies in the intersection of theoretical computer science and data mining. He is also interested in algorithmic fairness. In the past, he has enjoyed working on approximation algorithms and arithmetic circuit complexity.

     

    Please note that this virtual event, including attendees’ Zoom video, audio and screen name, and questions or chats, will be recorded. All or portions of the event recording may be shared through Brown University’s digital channels. Individuals who do not want their identities to be captured are solely responsible for turning off their camera, muting their microphone, and/or adjusting their screen name accordingly. By attending this event, you consent to your name, voice, and/or image being recorded and to Brown University reproducing, distributing, and otherwise displaying the recording, within its sole discretion.

     

    Data Wednesdays

    The Data Science Initiative joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science.

  • Dr. Feng Liang

    Associate Professor at the Department of Statistics, University of Illinois at Urbana-Champaign

    Title: Learning Topic Models: Identifiability and Rate of Convergence

    Abstract: Topic models provide a useful text-mining tool for learning, extracting, and discovering latent structures in large text corpora. Although a plethora of algorithms have been proposed for topic modeling, little work is done to study the statistical accuracy of the estimated structures. In this paper, we propose an MLE of latent topics based on an integrated likelihood. We further introduce a new set of conditions for topic model identifiability, which are weaker than conditions that reply to the existence of anchor words. In addition, we study the estimation consistency and establish the convergence rate of the proposed estimator. Our algorithm, which is an application of the EM algorithm, is demonstrated to have competitive performance through simulation studies and a real application.

    This is based on joint work with Yinyin Chen, Shishuang He, and Yun Yang.

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Fritz Lekschas
    Harvard University
    Please Note: Brown Login Required For This Talk
    Abstract: Visually exploring data is a powerful approach to discover, understand, and interpret novel or not-well defined patterns. It allows us to gain insights and generate hypotheses for subsequent analyses. However, visual exploration can become challenging when the patterns of interest are sparsely-distributed, several orders of magnitude smaller than the entire dataset, or detected with high uncertainty.

    In this talk, I will present new visualization systems and interaction techniques for efficiently browsing, comparing, and finding patterns in the context of genomic, geospatial, and time-series data. Specifically, I will describe a web platform for browsing multi-modal and multi-scale datasets, as well as their guided navigation. I will present a generalized framework and toolkit for interactively arranging, grouping, and aggregating thousands of pattern instances. And I will demonstrate how interactive visual machine learning can enhance our ability to find patterns effectively. In combining visualization and human-centered machine-learning, these systems ensure that human-in-the-loop data analysis remains feasible with increasingly-large and complex scientific datasets.
    Bio: Fritz Lekschas is a Ph.D. candidate in computer science at Harvard University, where he is advised by Hanspeter Pfister. His research focuses on the development of scalable visual exploration systems for analyzing patterns in biomedical data. Prior to his doctoral program, Fritz visited Harvard Medical School as a post-graduate research fellow to work with Nils Gehlenborg and Peter J. Park on ontology-guided exploration of biological data repositories. He earned his bachelor’s and master of science degrees in bioinformatics from the Freie Universität Berlin, Germany. Fritz’s work has been recognized with several awards, including a Siebel Scholars Award, the Best Paper Award from EuroVis 2020, and a Best Paper Honorable Mention from IEEE InfoVis 2020.
    Host: David Laidlaw
  • Focused on the effects of media on society – Harmony Labs envisions a world where media systems support healthy, democratic culture and healthy, happy people. Michael Slaby ’02 and Riki Conrey will share their unique approach to problem-solving and using data for social good. How does data and technology drive organizations and companies, and can media serve the public good? Register here to attend.

    The event is co-sponsored by the Nelson Center, the Watson Institute, and the Data Science Initiative (DSI) at Brown.

    Riki Conrey

    Riki is the Director of Science at Harmony Labs and comes to the team with an extensive background in data science for social good. Most known for her work on narrative research projects like Story at Scale and the Peoria Project, Riki has joined Harmony Labs to help researchers connect artists to the insights they need about their audiences. Riki holds a Ph.D. from Northwestern University in Social Psychology but only because data science wasn’t invented yet when she was being educated.

    Michael Slaby ’02

    Michael is chief strategist at Harmony Labs working on accelerating media reform and transformation. Previously, he founded and was head of mission of Timshel—a social impact technology company. He was a fellow at the Shorenstein Center at the Harvard Kennedy School of Government. Michael helped lead the Obama for America campaign as chief integration and innovation officer in 2012 where he oversaw all technology and analytics, and as deputy digital director and chief technology officer in 2008.

    Careers, Recruiting, Internships, Education, Teaching, Instruction, Entrepreneurship, Government, Public & International Affairs, Identity, Culture, Inclusion, Research
  • Vaggos Chatziafratis
    Google Research NY
    Please Note: Brown Login Required For This Talk
    Title: From Darwin to Deep Learning: A tale of Algorithms, Optimization and Chaos
    Abstract: In this talk, we shed new light on two basic questions in machine learning using ideas from approximation algorithms, optimization and dynamical systems.

    The first question concerns a popular tool in unsupervised learning that partitions a dataset in a hierarchical manner, called Hierarchical Clustering. Despite its long history and plethora of heuristics, a principled framework for understanding its optimization properties had been missing. Our work takes a formal approach and puts Hierarchical Clustering on a firm theoretical grounding, highlighting new connections to convex optimization and graph algorithms.


    The second question concerns the benefits of depth in neural networks. A crucial element in the success of deep learning is the deployment of progressively deeper networks, but is there a mathematical explanation behind this phenomenon? Introducing new ideas from discrete dynamical systems, we present depth vs width tradeoffs, showing that for certain tasks, depth can be exponentially more important than width.

    Bio: Vaggos Chatziafratis’ primary interests are in Algorithms and Machine Learning Theory. He is currently a Visiting Faculty Researcher at Google Research in New York, hosted by Mohammad Mahdian and Vahab Mirrokni, where he is part of the Algorithms and Graph Mining teams. Prior to that, he received his Ph.D. in Computer Science at Stanford, where he was part of the Theory group, advised by Tim Roughgarden and co-advised by Moses Charikar. His PhD thesis was on algorithms and their limitations for Hierarchical Clustering. Prior to Stanford, he received a Diploma in EECS from the National Technical University of Athens, Greece.
    Host: Daniel Ritchie
  • Sahil Singla
    Princeton University/Institute for Advanced Study
    Please Note: Brown Login Required For This Talk
     
    Abstract: Modern algorithms have to regularly deal with uncertain inputs. This uncertainty can take many forms, e.g., in online advertisement future users are unknown (the input arrives online), in spectrum-auctions bidder valuations are unknown (the users are strategic), and in oil-drilling the amount of oil is unknown (the input is stochastic). Traditionally, there has not been significant overlap in the study of these different forms of uncertainty. I believe that studying these uncertainties together gives us a lot more power. In this talk, I will give an overview of my research in “Algorithms and Uncertainty” where I am able to successfully use these relationships.

    Studying these different forms of uncertainty together allows us to find interconnections and build unifying techniques. As an example, I will talk about my progress on long-standing combinatorial auctions problems that deal with strategic inputs, by using techniques which were originally developed for online inputs. Moreover, a combined study of uncertainty helps us find richer cross-cutting models. For example, several important online problems do not admit good algorithms in the classical worst-case models. I will talk about how to give a “beyond the worst-case” analysis for such problems and obtain more nuanced performance guarantees, by using models/techniques arising in other forms of uncertainty.
    Bio: Sahil Singla is a Research Instructor (postdoc) jointly between Princeton University and Institute for Advanced Study. He received his Ph.D. in Computer Science from Carnegie Mellon University, where he was advised by Anupam Gupta and Manuel Blum. His research is in Algorithms and Uncertainty where the goal is to design optimal algorithms for uncertain inputs by studying different forms of uncertainty together. Sahil has served on the program committees of the conferences SODA, ICALP, EC, and ESA. His research has been invited for talks at Highlights Beyond EC, at China Theory Week, and at Highlights of Algorithms. He recently contributed a chapter to the book “Beyond the Worst-Case Analysis of Algorithms”.
    Host: Philip Klein
  •  

    CHARALAMPOS “BABIS” TSOURAKAKIS

    Assistant Professor, Departments of Computer Science and Electrical/Computer Engineering, Boston University

    ALGORITHMIC ADVANCES IN DENSE SUBGRAPH DISCOVERY

    Finding dense subgraphs in large-scale networks is an important graph mining problem with numerous applications, including anomaly detection in security, community detection in social networks, and mining the Web graph. In this talk, Charalampos will present recent advances related to the well-studied densest subgraph problem (DSP). Specifically, he will discuss some recent advances including how we can obtain a near-optimal solution to the DSP without maximum flows, motif-aware extensions of the DSP that enable the discovery of large-near cliques in massive networks, and risk-averse versions of the DSP on graphs whose edges are naturally associated with uncertainty.

     

    BIOGRAPHY

    Charalampos Tsourakakis is an Assistant Professor in Computer Science at Boston University. He obtained his Ph.D. in the Algorithms, Combinatorics and Optimization program at Carnegie Mellon under the supervision of Alan Frieze, he was a postdoctoral fellow at Brown University and Harvard University mentored by Eli Upfal and Michael Mitzenmacher respectively. Before joining Boston University, he worked as a researcher in the Google Brain team. He won a “best paper” award in IEEE Data Mining, has delivered three tutorials in the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, and has designed two graph mining libraries for large-scale graph mining, one of which has been officially included in Windows Azure. His research focuses on large-scale graph mining and machine learning.

     

    Please note that this virtual event, including attendees’ Zoom video, audio and screen name, and questions or chats, will be recorded. All or portions of the event recording may be shared through Brown University’s digital channels. Individuals who do not want their identities to be captured are solely responsible for turning off their camera, muting their microphone, and/or adjusting their screen name accordingly. By attending this event, you consent to your name, voice, and/or image being recorded and to Brown University reproducing, distributing, and otherwise displaying the recording, within its sole discretion.

     

    Data Wednesdays

    The Data Science Initiative joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science.

  • Shahin Jabbari
    Harvard University
    Please Note: Brown Login Required For This Talk
    Abstract: AI systems and algorithms affect and improve our lives on a daily basis. Recently, there has been a large body of empirical evidence for the negative consequences of such systems on our society. In this talk, I discuss two categories of these negative impacts. In the first part, I focus on AI systems whose decisions directly impact humans (e.g., in settings such as hiring and lending). I show how to mathematically quantify bias and discrimination in these settings, as well as how to build algorithmic frameworks that provably mitigate such undesirable consequences. In the second part, I focus on the indirect impacts of AI systems on humans. In particular, I study the interactions between AI systems and show how tools from game theory can be used to design systems that lead to socially desirable outcomes.
    Bio: Shahin Jabbari is a postdoctoral fellow at the Center for Research on Computation and Society (CRCS) in the school of Engineering and Applied Sciences at Harvard University hosted by Milind Tambe. Shahin’s research interests span across many areas including machine learning, game theory, crowd-sourcing, and multi-agent systems. His research mainly focuses on understanding and mitigating the negative impacts of algorithmic decision-making and artificial intelligence on society. Shahin completed his Ph.D. at the University of Pennsylvania in 2019 where he was advised by Michael Kearns. He is the recipient of the Best Paper Awards from the 11th Conference on Decision and Game Theory for Security, as well as the 35th Annual German Conference on Artificial Intelligence.
    Host: Amy Greenwald
  •    Akilah Dulin, PhD     Associate Professor of Behavioral and Social Sciences   Brown School of ...
    Mar
    3

    Join us for the final installment in this 3-part series exploring qualitative methods around the topics of design thinking, rapid ethnography, and concept mapping.

    This week: 

    Akilah Dulin, PhD
    Associate Professor, Center for Health Promotion and Health Equity, Brown University School of Public Health

    “Applying Concept Mapping Methodology To Understand Resilience Resources Among African Americans Living With HIV In The Southern United States”

    Dr. Dulin is an Associate Professor of Behavioral and Social Sciences in the Department of Behavioral and Social Sciences in the Brown University School of Public Health. She is affiliated with the Center for Health Promotion and Health Equity. Dr. Dulin has two primary research foci that include examining (1) risk factors and resilience resources related to cardiovascular disease (CVD) risk (e.g., diet, physical activity, obesity) and HIV outcomes among racial and ethnic minorities and across the life span. She uses a variety of research approaches to examine the aforementioned research areas including community engaged research, mixed methods, quantitative methods, and secondary data analyses.

    Biology, Medicine, Public Health, Entrepreneurship, Psychology & Cognitive Sciences, Research
  • Mar
    2
    12:00pm - 1:30pm

    Advance-CTR Introduction to REDCap

    Zoom

    Register now for our “Introduction to REDCap” virtual workshop with Sarah B. Andrea, PhD, MPH. Geared towards new or novice REDCap users, this class answers “What” REDCap is, “Why” you want to use it, and goes through the entire lifecycle of a REDCap project – from initial setup to data entry and finally exporting your data. The workshop will begin Tuesday March 2nd, 2020 at 12:00PM EST.

    Biology, Medicine, Public Health, Entrepreneurship, Research, Training, Professional Development
  • Marina Vannucci, PhD

    Noah Harding Professor of Statistics, Rice University

    Title: Dirichlet-Multinomial Regression Models with Bayesian Variable Selection for Microbiome Data

    Abstract:

    I will describe Bayesian models developed for understanding how the microbiome varies within a population of interest. I will focus on integrative analyses, where the goal is to combine microbiome data with other available information (e.g. dietary patterns) to identify significant associations between taxa and a set of predictors. For this, I will describe a general class of hierarchical Dirichlet-Multinomial (DM) regression models which use spike-and-slab priors for the selection of the significant associations. I will also describe data augmentation techniques to efficiently embed DM regression models into joint modeling frameworks, in order to investigate how the microbiome may affect the relation between dietary factors and phenotypic responses, such as body mass index. I will discuss advantages and limitations of the proposed methods with respect to current standard approaches used in the microbiome community, and will present results on the analysis of real datasets.

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Zhuoran Yang
    Princeton University
    Please Note: Brown Login Required For This Talk
    Abstract: Coupled with powerful function approximators such as deep neural networks, reinforcement learning (RL) achieves tremendous empirical successes. However, its theoretical
    understandings lag behind. In particular, it remains unclear how to provably attain the optimal
    policy with a finite regret or sample complexity. In this talk, we will present the two sides of the
    same coin, which demonstrates an intriguing duality between optimism and pessimism.
    - In the online setting, we aim to learn the optimal policy by actively interacting with an environment. To strike a balance between exploration and exploitation, we propose an optimistic least-squares value iteration algorithm, which achieves a \sqrt regret in the presence of linear, kernel, and neural function approximators.
    - In the offline setting, we aim to learn the optimal policy based on a dataset collected a priori.
    Due to a lack of active interactions with the environment, we suffer from the insufficient coverage of the dataset. To maximally exploit the dataset, we propose a pessimistic least-squares value iteration algorithm, which achieves a minimax-optimal sample complexity.
    Bio: Zhuoran Yang is a final-year Ph.D. student in the Department of Operations Research and
    Financial Engineering at Princeton University, advised by Professor Jianqing Fan and Professor Han Liu. Before attending Princeton, He obtained a Bachelor of Mathematics degree from Tsinghua University. His research interests lie in the interface between machine learning, statistics, and optimization. The primary goal of his research is to design a new generation of machine learning algorithms for large-scale and multi-agent decision-making problems, with both statistical and computational guarantees. Besides, he is also interested in the application of learning-based decision-making algorithms to real-world problems that arise in robotics, personalized medicine, and computational social science.
    Host: Roberta De Vito
  • Zhuoran Yang
    Princeton University
    Please Note: Brown Login Required For This Talk
    Abstract: Coupled with powerful function approximators such as deep neural networks, reinforcement learning (RL) achieves tremendous empirical successes. However, its theoretical
    understandings lag behind. In particular, it remains unclear how to provably attain the optimal
    policy with a finite regret or sample complexity. In this talk, we will present the two sides of the
    same coin, which demonstrates an intriguing duality between optimism and pessimism.
    - In the online setting, we aim to learn the optimal policy by actively interacting with an environment. To strike a balance between exploration and exploitation, we propose an optimistic least-squares value iteration algorithm, which achieves a \sqrt regret in the presence of linear, kernel, and neural function approximators.
    - In the offline setting, we aim to learn the optimal policy based on a dataset collected a priori.
    Due to a lack of active interactions with the environment, we suffer from the insufficient coverage of the dataset. To maximally exploit the dataset, we propose a pessimistic least-squares value iteration algorithm, which achieves a minimax-optimal sample complexity.
    Bio: Zhuoran Yang is a final-year Ph.D. student in the Department of Operations Research and
    Financial Engineering at Princeton University, advised by Professor Jianqing Fan and Professor Han Liu. Before attending Princeton, He obtained a Bachelor of Mathematics degree from Tsinghua University. His research interests lie in the interface between machine learning, statistics, and optimization. The primary goal of his research is to design a new generation of machine learning algorithms for large-scale and multi-agent decision-making problems, with both statistical and computational guarantees. Besides, he is also interested in the application of learning-based decision-making algorithms to real-world problems that arise in robotics, personalized medicine, and computational social science.
    Host: Roberta De Vito
  • Riad Wahby
    Stanford University
    Please Note: Brown Login Required For This Talk
     
    Abstract: In the past decade, systems that use probabilistic proofs in real-world
    applications have seen explosive growth. These systems build upon some
    of the crown jewels of theoretical computer science—interactive proofs,
    probabilistically checkable proofs, and zero-knowledge proofs—to solve
    problems of trust and privacy in a wide range of settings.

    This talk describes my work building systems that answer questions ranging
    from “how can we build trustworthy hardware that uses untrusted components?”
    to “how can we reduce the cost of verifying smart contract execution in
    blockchains?” Along the way, I will discuss the pervasive challenges of
    efficiency, expressiveness, and scalability in this research area; my approach
    to addressing these challenges; and future directions that promise to bring
    this exciting technology to bear on an even wider range of applications.
    Bio: Riad S. Wahby is a Ph.D. candidate at Stanford, advised by Dan Boneh and
    Keith Winstein. His research interests include systems, computer security,
    and applied cryptography. Prior to attending Stanford, Riad spent ten years
    as an analog and mixed-signal integrated circuit designer. Riad and his
    collaborators received a 2016 IEEE Security and Privacy Distinguished Student
    Paper award; his work on hashing to elliptic curves is being standardized
    by the IETF.
    Host: Vasilis Kemerlis
  • Please join us on Thursday, February 25, at 4 p.m. for “Kinder-ready clinics: Emerging models of how clinics can support parents in early-childhood development through low-cost, scalable interventions,” presented by Susanna Loeb, PhD, and Lisa Chamberlain, MD, MPH.

    Dr. Loeb is director of the Annenberg Institute for School Reform and professor of education and of international and public affairs at Brown University. Dr. Loeb is a member of the Executive Council of The Policy Lab at Brown.

    Dr. Chamberlain is associate professor of pediatrics, associate chair of policy and community and the Arline and Pete Harman Faculty Scholar at Stanford Children’s Hospital. She founded and co-directs the Stanford Pediatric Advocacy Program to train leaders in community pediatrics and advocacy.

    Please register below to receive the Zoom link for this virtual event.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Research, Teaching & Learning
  • Ben Fish
    Mila
    Please Note: Brown Login Required For This Talk
    Title: Defining and Ensuring Algorithmic Fairness in Artificial Intelligence
    Abstract: Artificial intelligence is increasingly used to make decisions about people in social domains. Failure to take into account its effects on people’s lives risks grave consequences, including enacting and perpetuating discrimination, and more broadly creating AI systems imbued with values we do not intend or desire. In this talk, I will detail the development over the last few years of increasingly sophisticated approaches to formalize and understand the normative impact of AI, and my own contributions to these approaches. Using examples from binary classification, influence maximization, and hiring markets, I will illustrate through theory and experiments the impact that considerations of fairness have in creating and analyzing algorithms. I will provide algorithms for ensuring group-level fairness in binary classification problems, algorithms for how to more equitably spread information in a social network, and a new approach to defining fairness in hiring markets. This work demonstrates how explicit mathematical modeling of the social impact of decision making can reveal new ways to capture the moral impacts of AI, and emphasizes that further progress in this area will be made by creating AI specifically for the surrounding social context in which it is embedded.
    Bio: Ben Fish is a postdoctoral fellow at Mila hosted by Fernando Diaz, which he joined after moving from the Fairness, Accountability, Transparency, and Ethics (FATE) Group at Microsoft Research Montréal, also hosted by Fernando Diaz. His research develops methods for machine learning and other computational systems that incorporate human values and social context. This includes scholarship in fairness and ethics in machine learning and learning over social networks. He received his Ph.D. from the University of Illinois at Chicago as a member of the Mathematical Computer Science group. He was previously a visiting researcher at the University of Melbourne and the University of Utah, and earned a B.A. from Pomona College in Mathematics and Computer Science.
    Host: Seny Kamara
  •  

    ROBERT GHRIST

    Professor, Departments of Mathematics and Electrical Systems/Engineering, University of Pennsylvania

     

    OPINION DYNAMICS ON SHEAVES

    There is a long history of networked dynamical systems that models the spread of opinions over social networks, with the graph Laplacian playing a lead role. One of the difficulties in modeling opinion dynamics is the presence of polarization: not everyone comes to a consensus. This talk will describe work joint with Jakob Hansen [OSU] introducing a new model for opinion dynamics using sheaves of vector spaces over social networks. The graph Laplacian is enriched to a Hodge Laplacian, and the resulting dynamics on *discourse sheaves* can lead to some very interesting and perhaps more realistic outcomes.

     

    BIOGRAPHY

    Robert Ghrist is the Andrea Mitchell PIK Professor of Mathematics and Electrical & Systems Engineering at the University of Pennsylvania. After earning a BS in Mechanical Engineering (University of Toledo, 1991), and the MS and Ph.D. in Applied Mathematics (Cornell University, 1994, 1995), he held positions in Mathematics departments at the University of Texas (Austin), Georgia Tech, and the University of Illinois (Urbana-Champaign). He has been at Penn since 2008.

    Ghrist is a recognized leader in the field of Applied Algebraic Topology, with publications detailing topological methods for sensor networks, robotics, signal processing, data analysis, optimization, and more. He is the author of a leading textbook on the subject (Elementary Applied Topology, 2014), and has managed numerous large DoD grants from AFOSR, ASDRE, DARPA, and ONR.

    His research has been recognized with the NSF CAREER, NSF PECASE, SciAm50, and Vannevar Bush Faculty Fellow awards. Ghrist has been an invited speaker at two International Congresses of Mathematicians: once (Madrid 2006) for research and once (Seoul, 2014) for education. Ghrist is a dedicated expositor and communicator of Mathematics, with teaching awards that include the MAA James Crawford Prize, Penn’s Lindback Award, and the S. Reid Warren Award in Engineering at Penn. Ghrist is the author, designer, and animator of popular YouTube video texts (featuring the Calculus BLUE Project), as well as an online course on Coursera, featured in the New York Times, BoingBoing, and Gizmodo.

     

    Please note that this virtual event, including attendees’ Zoom video, audio and screen name, and questions or chats, will be recorded. All or portions of the event recording may be shared through Brown University’s digital channels. Individuals who do not want their identities to be captured are solely responsible for turning off their camera, muting their microphone, and/or adjusting their screen name accordingly. By attending this event, you consent to your name, voice, and/or image being recorded and to Brown University reproducing, distributing, and otherwise displaying the recording, within its sole discretion.

     

    Data Wednesdays

    The Data Science Initiative joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science.

  • Fraser Brown
    Stanford University
    Please Note: Brown Login Required For This Talk
    Title: Eliminating bugs in real systems.
    Abstract: Software is everywhere, and almost everywhere, software is broken. Some bugs just crash your printer; others hand an identity thief your bank account number; still others let nation-states spy on dissidents and persecute minorities.
    This talk outlines my work preventing bugs using a blend of programming languages techniques and systems design. First, I’ll talk about securing massive, security-critical codebases without clean slate rewrites. This means rooting out hard-to-find bugs—as in Sys, which scales symbolic execution to find exploitable bugs in systems like the twenty-million line Chrome browser. It also means proving correctness of especially vulnerable pieces of code—as in VeRA, which automatically verifies part of the Firefox JavaScript engine. Finally, I’ll discuss work on stronger foundations for new systems—as in CirC, a recent project unifying compiler infrastructure for program verification, cryptographic proofs, optimization problems, and more.
    Bio: Fraser Brown is a PhD student at Stanford advised by Dawson Engler, occasional visiting student at UCSD with Deian Stefan, and NSF graduate research fellowship recipient. She works at the intersection of programming languages, systems, and security, and her research has been used by several companies. She holds an undergraduate degree in English from Stanford.
    Host: Malte Schwarzkopf
  • Join us for this 3-part series exploring qualitative methods around the topics of design thinking, rapid ethnography, and concept mapping.

    Wednesday, February 24, 2021

    An Introduction To Rapid Ethnography: Its Applications & Utility In Public Health Research

    This presentation will provide an overview of ethnographic research, focusing on the role of rapid ethnography in public health research, and how it can be utilized in community-based research, public health evaluations, and during public health crises.

    Alexandra Collins, PhD
    Postdoctoral Research Associate, Brown University School of Public Health

    Dr. Alexandra Collins is a Postdoctoral Research Associate in the Department of Epidemiology and Brown University’s School of Public Health. She received her PhD in Health Sciences with a focus on medical social sciences and applied anthropology from Simon Fraser University in Vancouver, Canada. Her community-engaged research focuses broadly on social, structural, and built environmental drivers of overdose risk, drug use risk environments, and evaluations of harm reduction interventions.

    Biology, Medicine, Public Health, Entrepreneurship, Psychology & Cognitive Sciences, Research, Training, Professional Development
  • Data Wednesday (1 of 2) @ 10 AM: (Pontus Skoglund, Crick Institute)

    PONTUS SKUGLAND

    Group Leader, Ancient Genomics Laboratory, Crick Institute

    TRACKING THE GENOMIC HISTORY OF DOGS AND HUMANS

    (Zoom Link)

     

    Data Wednesday (2 of 2) @ 4 PM: (Robert Ghrist, U Penn)

    ROBERT GHRIST

    Professor, Departments of Mathematics and Electrical Systems/Engineering, University of Pennsylvania 

    OPINION DYNAMICS ON SHEAVES

    (Zoom Link)

     

     
    Please note that this virtual event, including attendees’ Zoom video, audio and screen name, and questions or chats, will be recorded. All or portions of the event recording may be shared through Brown University’s digital channels. Individuals who do not want their identities to be captured are solely responsible for turning off their camera, muting their microphone, and/or adjusting their screen name accordingly. By attending this event, you consent to your name, voice, and/or image being recorded and to Brown University reproducing, distributing, and otherwise displaying the recording, within its sole discretion.

     

    Data Wednesdays

    The Data Science Initiative joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science.

  •  

    PONTUS SKUGLAND

    Group Leader, Ancient Genomics Laboratory, Crick Institute

    TRACKING THE GENOMIC HISTORY OF DOGS AND HUMANS

    Dogs were the first domestic animal, but little is known about their population history and to what extent it was linked to humans. I will discuss recent evidence from ancient dog genomes of limited gene flow from wolves since domestication, but substantial dog-to-wolf gene flow. By 11,000 years ago, at least five 5 major ancestry lineages had already diversified, demonstrating a deep genetic history of dogs during the Paleolithic. Co-analysis with human genomes reveals aspects of dog population history that mirror humans, including Levant-related ancestry in Africa and early agricultural Europe. Other aspects differ, including the impacts of steppe pastoralist expansions in West- and East Eurasia, and a complete turnover of Neolithic European dog ancestry. 

     

    BIOGRAPHY

    Pontus Skoglund is the group leader of the Francis Crick Institute’s Ancient Genomics laboratory. Originally from Sweden, he obtained his Ph.D. in evolutionary genetics from Uppsala University in 2013 and thereafter did his postdoctoral research in David Reich’s laboratory at Harvard Medical School’s Department of Genetics.

    His past research has focused on propelling the field of ancient DNA into the genomic era, revealing population migrations as catalyzers for the transition from hunter-gatherer lifestyles to agriculture in Europe, Africa, and Southeast Asia. He has also studied gene flow between archaic-and-modern humans, early human evolution in Africa, the peopling of the Americas, and the origin of domestic dogs.

     

    Please note that this virtual event, including attendees’ Zoom video, audio and screen name, and questions or chats, will be recorded. All or portions of the event recording may be shared through Brown University’s digital channels. Individuals who do not want their identities to be captured are solely responsible for turning off their camera, muting their microphone, and/or adjusting their screen name accordingly. By attending this event, you consent to your name, voice, and/or image being recorded and to Brown University reproducing, distributing, and otherwise displaying the recording, within its sole discretion.

     

    Data Wednesdays

    The Data Science Initiative joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science.

  • Feb
    22
    Jan van den Brand
    KTH Royal Institute of Technology
    Please Note: Brown Login Required For This Talk
    Title: Dynamic Linear Algebra
    Abstract: Dynamic linear algebra—algorithmic techniques for matrices that change over time—lies at the core of many applications, from continuous optimization, to efficient graph algorithms, to machine learning. Yet, until recently, the full power of dynamic linear algebra was not known and exploited in most applications.
    In this talk, I will describe several new advances in using these techniques and outline the limits of what can be done with them. I will overview progress on longstanding problems in dynamic shortest path data structures, regression algorithms, optimal transport and other problems, using dynamic linear algebra.
    Bio: Jan van den Brand obtained his bachelor’s and master’s degrees in both mathematics and computer science at the Goethe University Frankfurt. Currently, he is a PhD candidate at KTH Royal Institute of Technology, Stockholm, and recipient of the Google PhD Fellowship. His research is on efficient algorithms with focus on optimization and dynamic problems.
    Host: Philip Klein
  •  PLEASE NOTE THAT THIS TALK IS FOR FACULTY ONLY.



    Register in advance for this meeting:
    https://brown.zoom.us/meeting/register/tJIocO-rrDgpHdzxRKvkqbRYLqK_i10i787E

    After registering, you will receive a confirmation email containing information about joining the meeting.

     

    RITAMBHARA SINGH

    Assistant Professor, Computer Science; Faculty, CCMB, Brown University

     

    TOWARDS DATA INTEGRATION IN GENOMICS USING MACHINE LEARNING

    Our current understanding of how genes are regulated is akin to solving a jigsaw puzzle. Many factors governing gene expression have been identified and researchers have collected a wide variety of related datasets. However, how these “parts” are pieced together to function as a whole remains unclear. In this talk, I will be discussing our research to develop and apply state-of-the-art machine learning methods to genomics datasets to attempt to put together the pieces from the data. I will discuss our work using deep learning architecture that captures the data’s underlying structure to integrate datasets and connect them to gene expression via the prediction task. We also interpret the prediction results and tie them back to contributing factors to develop potential hypotheses related to gene regulation. I will then move from bulk data to the single-cell data domain discussing methods to perform unsupervised integration of different types of single-cell experiments. This talk aims to highlight our research direction’s potential to reveal the important gene regulatory mechanisms in characterizing diseases from the collected data.

    BIOGRAPHY

    Ritambhara Singh is an Assistant Professor of the Computer Science department and a faculty member of the Center for Computational Molecular Biology at Brown University. Her research lab works at the intersection of machine learning and biology. Prior to joining Brown, Singh was a post-doctoral researcher in the Noble Lab at the University of Washington. She completed her Ph.D. in 2018 from the University of Virginia with Dr. Yanjun Qi as her advisor. Her research has involved developing machine learning algorithms for the analysis of biological data as well as applying deep learning models to novel biological applications.

    Please note that this virtual event, including attendees’ Zoom video, audio and screen name, and questions or chats, will be recorded. All or portions of the event recording may be shared through Brown University’s digital channels. Individuals who do not want their identities to be captured are solely responsible for turning off their camera, muting their microphone and/or adjusting their screen name accordingly. By attending this event, you consent to your name, voice, and/or image being recorded and to Brown University reproducing, distributing and otherwise displaying the recording, within its sole discretion.

     

    Fair February

    This is an opportunity for faculty to share current data science-related research activities with other faculty colleagues in an informal and interdisciplinary environment. More about this series on our website This event is organized by Professor Meenakshi Narain, DSI Advisory Board Co-chair.

  • Thodoris Lykouris
    Microsoft Research NYC
    Please Note: Brown Login Required For This Talk
    Abstract: Modern online marketplaces require decisions to be made sequentially. These decisions do not only affect the system’s performance on the current customer but may also have long-lasting effects, giving rise to a sequence of novel challenges.
    In this talk, I will focus on one example of such challenges: the need of robustness to data corruption and other model misspecifications. Classical machine learning approaches rely on collecting a batch of data and fitting a model to it – this assumes that customers’ behavior is identically and independently distributed. However, in practice, the behavioral models assumed are often slightly misspecified, e.g., due to the strategic behavior of participating entities. Motivated by this practical concern, I will focus on two canonical revenue management settings (online advertising and feature-based dynamic pricing) and will introduce an algorithmic framework for achieving robustness to such model misspecifications.
    I will end the talk by discussing my broader research agenda on dealing with other practical and societal challenges that arise in sequential decision-making settings where data and decisions are inherently intertwined.
    Bio: Thodoris Lykouris is a postdoctoral researcher in the machine learning group of Microsoft Research NYC. His research focus is on data-driven sequential decision-making and spans across the disciplines of machine learning, operations research, theoretical computer science, and economics. He completed his Ph.D. in 2019 from Cornell University where he was advised by Éva Tardos. His dissertation was selected as a finalist in the Dantzig dissertation award competition. He was also a finalist in the INFORMS Nicholson and Applied Probability Society best student paper competitions. Thodoris is the recipient of a Google Ph.D. Fellowship and a Cornell University Fellowship.
    Host: Amy Greenwald
  • I2S2 Seminar

    D2R and EHRs: A success Story from an Academic Healthcare Setting

     

    Featuring Dr. Fizza Gillani, PhD, CPEHR

    Picture of Dr. Fizza Gillani, Associate Professor of Medicine (Research), Warren Alpert Medical S...Picture of Dr. Fizza Gillani, Associate Professor of Medicine (Research), Warren Alpert Medical School, Brown University;Informatics Director for HIV/AIDS Program, Providence-Boston Center for AIDS Research, and the Ryan White Program; and Senior Research Scientist at Lifespan.

    HIV Care Continuum requires a multidisciplinary approach to achieve clinical outcomes that help control HIV disease progression. To track these outcomes, we need a robust informatics system with a clearly defined Data to Research and Reporting (D2R) approach to make use of different Electronic Health Records (EHRs). The Ryan White-funded Miriam Hospital Immunology Center (MIC) is part of an academic medical system with more than 15,000 employees, 4 hospitals, and a well-established Information System department. To support the MIC HIV program, a robust HIV-specific Immunology Center Database (ICDB) was created in 2003. The ICDB system is an example of how carefully planned data systems built around existing health IT infrastructure provide evidence of best practices, measure performance as feedback for healthcare systems, and advance us closer to realizing the vision of a learning health system. ICDB in its current format is centered on the system-wide electronic health record and technology platforms, supporting reporting and research requirements determined by the federal Ryan White program, different funding mechanisms, and local governments.

    This presentation will demonstrate how carefully integrated information systems can achieve the goals of tracking progress of healthcare outcomes, properly initiating quality initiatives, generating performance measures, supporting accurate government reporting, and expediting research initiatives.

     

     

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research
  •  
     
    KELLEY HARRIS

    Assistant Professor, Department of Genome Sciences, University of Washington-Seattle

    A WILD-DERIVED MUTATOR ALLELE DRIVES MUTATION SPECTRUM DIFFERENCES AMONG COMMON LABORATORY MOUSE STRAINS

    Although eukaryotic genomes are safeguarded by hundreds of DNA replication and repair genes, it has proven difficult to study the functional consequences of variation within the sequences of these genes. Some large-effect DNA repair gene variants are known to cause heritable cancer syndromes and accelerate somatic mutagenesis, but it is not known whether such variants might cause germline mutation rates to vary within populations. We performed a QTL scan for germline mutator alleles in a uniquely powerful vertebrate system: a panel of 98 recombinant mouse strains that have each been inbred in captivity for up to 45 years, accumulating many generations’ worth of de novo mutations on known genetic backgrounds. The scan identified a locus that strongly affects the rate of C>A germline mutation accumulation, specifically in the sequence contexts CA>AA and CT>TT. We identify candidate causal variation in the gene Mutyh, which causes a human cancer syndrome associated with a similar mutational signature. This Mutyh variation also segregates in wild populations of Mus musculus domesticus, where it may be shaping the accumulation of natural genetic variation.

    BIOGRAPHY

    Kelley Harris is an Assistant Professor in the Department of Genome Sciences at the University of Washington in Seattle. 

    She uses population genetic theory and high-throughput biological sequence analysis to study recent evolutionary history in humans and other species. One of her primary research interests is the evolution of mutagenesis; she wants to understand the forces that control DNA replication fidelity, the mutational breakdown of established traits, and the ultimate origin of new traits. Her lab will work to decipher how variations are genetically and environmentally determined and what evolutionary pressures (such as cancer, congenital disease, or life history) might be driving mutagenesis to change.

    She is also broadly interested in the impact of demography, inbreeding, and hybridization on the dynamics of natural selection, particularly in the wake of gene flow between humans, Neanderthals, and other extinct hominids. Harris has developed a variety of computational methods for inferring population bottlenecks, divergence times, and admixture events at high resolution, and has written about the impact of Neanderthal interbreeding on the fitness of archaic and modern humans. Her group will continue developing new statistical models that refine our understanding of how genomes and populations evolve, using methods derived from coalescent theory to visualize and extract the information contained in huge databases of whole genomes.

    She accepts graduate students through UW’s Genome Sciences Ph.D. program and is looking for motivated postdoctoral fellowship candidates with experience in bioinformatics and/or population genetics.

     

    Please note that this virtual event, including attendees’ Zoom video, audio and screen name, and questions or chats, will be recorded. All or portions of the event recording may be shared through Brown University’s digital channels. Individuals who do not want their identities to be captured are solely responsible for turning off their camera, muting their microphone, and/or adjusting their screen name accordingly. By attending this event, you consent to your name, voice, and/or image being recorded and to Brown University reproducing, distributing, and otherwise displaying the recording, within its sole discretion.

     

    Data Wednesdays

    The Data Science Initiative joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science.

  • Please join us on Wednesday, February 17th for a seminar series presentation by Elad Yom-Tov, PhD: “An adaptive messaging intervention to increase physical activity.” Despite the clear benefit of regular physical activity, most patients with diabetes type 2 are sedentary. Smartphones and wearable devices have become ubiquitous in modern societies, bringing new opportunities for novel interventions through continuous monitoring of patients and timely communication with them. However, efforts to encourage activity of these patients using smartphones have focused on predetermined messages or messages tailored by experts, limiting their effectiveness.

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research, Social Sciences
  • Join us for this 3-part series exploring qualitative methods around the topics of design thinking, rapid ethnography, and concept mapping.

    Wednesday, February 17, 2021

    The Power of Designing with Patients

    Aaron J. Horowitz
    Co-Founder & CEO
    Sproutel

    Aaron is a maker; from sculptures to business, he is fascinated with the process of taking an idea from concept to reality. His experience growing up with human growth hormone deficiency inspired a desire to bring empathy, design, and a patient-centered mindset to healthcare. He is the co-founder and CEO of Sproutel, a research and development workshop focused on creating play-based healthcare innovations. Sproutel is best known for their work collaborating with Aflac to create My Special Aflac Duck, a robotic companion for children with cancer!

    Biology, Medicine, Public Health, Entrepreneurship, Psychology & Cognitive Sciences, Research, Training, Professional Development
  • Allison Koenecke
    Stanford University
    Algorithmically guided decisions are becoming increasingly prevalent and, if left unchecked, can amplify pre-existing societal biases. In this talk, I use modern computational tools to examine the equity of decision-making in two complex systems: automated speech recognition and online advertising. In the former, I demonstrate large racial disparities in the performance of popular commercial speech-to-text systems developed by Amazon, Apple, Google, IBM, and Microsoft, a pattern likely stemming from a lack of diversity in the data used to train the systems. These results point to hurdles faced by African Americans in using widespread tools driven by speech recognition technology. In the second part of the talk, I propose a methodological framework for online advertisers to determine a demographically equitable allocation of individuals being shown ads for SNAP (food stamp) benefits. In particular, I discuss how to formulate fair decisions considering budget-constrained trade-offs between English-speaking and Spanish-speaking SNAP applicants. Both projects exemplify processes to ameliorate demographic-based disparate impact arising from decisions made by online platforms.
    Allison Koenecke is a PhD candidate at Stanford’s Institute for Computational & Mathematical Engineering. Her research interests lie broadly at the intersection of economics and computer science, and her projects focus on algorithmic fairness in online applications and causal inference in public health. She previously specialized in antitrust at NERA Economic Consulting after graduating from MIT with a Bachelor’s in Mathematics with Computer Science.
    Host: Professor Ellie Pavlick
  • Please use Brown email account to join
    Allison Koenecke

    Stanford University

    Algorithmically guided decisions are becoming increasingly prevalent and, if left unchecked, can amplify pre-existing societal biases. In this talk, I use modern computational tools to examine the equity of decision-making in two complex systems: automated speech recognition and online advertising. In the former, I demonstrate large racial disparities in the performance of popular commercial speech-to-text systems developed by Amazon, Apple, Google, IBM, and Microsoft, a pattern likely stemming from a lack of diversity in the data used to train the systems. These results point to hurdles faced by African Americans in using widespread tools driven by speech recognition technology. In the second part of the talk, I propose a methodological framework for online advertisers to determine a demographically equitable allocation of individuals being shown ads for SNAP (food stamp) benefits. In particular, I discuss how to formulate fair decisions considering budget-constrained trade-offs between English-speaking and Spanish-speaking SNAP applicants. Both projects exemplify processes to ameliorate demographic-based disparate impact arising from decisions made by online platforms.

     

    Allison Koenecke is a PhD candidate at Stanford’s Institute for Computational & Mathematical Engineering. Her research interests lie broadly at the intersection of economics and computer science, and her projects focus on algorithmic fairness in online applications and causal inference in public health. She previously specialized in antitrust at NERA Economic Consulting after graduating from MIT with a Bachelor’s in Mathematics with Computer Science.

     

    Host: Professor Ellie Pavlick
  • Highlights from the Advance-CTR Informatics Core: REDCap and N3C

     

    Dr. Karen Crowley, Manager of Health Data Science, Advance-CTR Informatics Core and the Brown Center for Biomedical Informatics, will provide an overview of the services and resources available through the Informatics Core with a special focus on our unique implementation of REDCap, a secure web application for building and managing online surveys and databases. She will also highlight N3C, the National COVID Cohort Collaborative, how Advance-CTR is participating and the plan to support researchers who wish to access this unique dataset.

     

    Dr. Karen M. Crowley is Manager of Health Data Science for the Brown Center for Biomedical Informatics (BCBI) and the Advance-CTR Biomedical Informatics, Bioinformatics, and Cyberinfrastructure Enhancement (BIBCE) Core. Dr. Crowley holds a Master of Science degree in Organizational Behavior and is a formally trained biomedical informatician with a PhD from the University of Utah. With experience in both industry and academia, Dr. Crowley has a special interest in applying her expertise in healthcare data, computing, and technology as well as organization dynamics and processes in support of the Learning Health System.

     

    The Informatics and Implementation Science Learning Series (I2S2) covers the breadth of topics in effectively using data and technology to advance biomedical discovery and healthcare delivery. Each learning activity (seminar, journal club, workshop, or tutorial) features methods, applications, or resources that are aligned with components of a learning health system. This series is a joint initiative between the Brown Center for Biomedical Informatics, Brown Department of Psychiatry and Human Behavior Implementation Science Core, Rhode Island Quality Institute, and Advance Clinical and Translational Research (Advance-CTR).

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research
  • Fair February: Data Science for Social Good

    Fair February is a three-week symposium organized by Brown’s Data Science Initiative. Each week of this symposium concentrates on a theme. The purpose of this symposium is to let young researchers of various disciplines interested in any of our themes, meet each other, and know about each other’s work and research.

    WEEK THREE: COMPUTATION AND DEMOCRACY

     

    SESSION ONE SPEAKERS

     

    DIVERSITY AND INEQUALITY IN SOCIAL NETWORKS: FROM RECOMMENDATION TO INFORMATION DIFFUSION

    Ana-Andreea Stoica, Ph.D. Candidate, Columbia University

    This talk is scheduled for 11:35am.

     

    THE EFFECT OF HOMOPHILY ON DISPARATE VISIBILITY OF MINORITIES IN PEOPLE RECOMMENDER SYSTEMS

    Francesco Fabbri, Ph.D. Candidate, Web Science and Social Computing Group, Pompeu Fabra University-Barcelona

    This talk is scheduled for 12:10pm.

     

    SESSION TWO SPEAKERS

     

    FAIR ALGORITHMS FOR CLUSTERING

    Suman Bera, Postdoctoral Research Associate, University of California-Santa Cruz

    This talk is scheduled for 2:05pm.

     

    AUDITING WIKIPEDIA’S HYPERLINKS NETWORK ON POLARIZING TOPICS

    Cristina Menghini,  Ph.D. Candidate, Sapienza University

    This talk is scheduled for 2:30pm.

     

    REDUCING STRUCTURAL BIAS IN NETWORKS BY LINK INSERTION

    Shahrzad Haddadan, Postdoctoral Research Associate, Data Science Initiative and Department of Computer Science, Brown University

    This talk is scheduled for 3:00pm.

     

    For more information, please contact Shahrzad Haddadan. To see more of Fair February’s events and speakers, visit our main event page.

    This series is organized by Shahrzad Haddadan, Marie Schenk, and Cristina Menghini. Sponsored by the Data Science Initiative.

    See the full schedule here.

     

  • Join the Carney Institute for Brain Science in conjunction with Love Data Week for a Carney Methods Meetup, an informal gathering focused on methods for brain science, on Thursday, February 11, at 3 p.m.

    This event will be moderated by Jason Ritt, Carney’s scientific director of quantitative neuroscience, and feature Samuel Watson, director of graduate studies for the Data Science Initiative.

    Please note, this workshop requires you to be logged into Zoom through your Brown account.

    Notes from previous Meetups are available online.

    Biology, Medicine, Public Health, CCBS, Psychology & Cognitive Sciences, Research, Teaching & Learning
  • Jules Gimbrone
    Feb
    11

    Love Data Week at BAI brings together Marie Thompson, author of Beyond Unwanted Sound: Noise, Affect and Aesthetic Moralism, and artist Jules Gimbrone, who will have an exhibition at BAI’s Cohen Gallery in fall 2021, to discuss noise as a product of data mediation, the sociality of listening, and the impossibility of quantifying gender, among many other intersections in their work. Moderated by Kate Kraczon, Acting Director and Curator, David Winton Bell Gallery, Brown University. 

    Jules Gimbrone is a visual and sonic artist based in New York, NY. Their work has appeared internationally in a variety of venues including the Walker Art Center, Minneapolis, MN; Sculpture Center, MoMA PS1, Rubin Museum of Art, Socrates Sculpture Park, and Judson Church, New York; and Los Angeles Contemporary Exhibitions, Human Resources LA, and LAXART, Los Angeles.

    Marie Thompson is a researcher living in Nottingham, UK. She is a Lecturer in Music at The Open University. Marie is the author of Beyond Unwanted Sound: Noise, Affect and Aesthetic Moralism (Bloomsbury, 2017) and is the Principal Investigator of the Arts and Humanities Research Council project, Tinnitus, Auditory Knowledge and the Arts. 

  • The Advance-CTR Translational Research Seminar Series showcases clinical and translational research from across Rhode Island. Presentations, followed by feedback, allow presenters the opportunity to refine and strengthen their research. Seminars are held virtually on the second Thursday of each month.

    February

    Details: February 11, 2021 at 12 p.m. ET.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Entrepreneurship, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research, Teaching & Learning, Training, Professional Development
  • Please join the Office of the General Counsel (OGC) for a discussion on the Family Educational Rights and Privacy Act (FERPA). The discussion is open to anyone and is geared to those who have limited knowledge of FERPA

    Graduate School, Postgraduate Education, OVPR, Research, Training, Professional Development
  • Fair February: Data Science for Social Good

    Fair February is a three-week symposium organized by Brown’s Data Science Initiative. Each week of this symposium concentrates on a theme. The purpose of this symposium is to let young researchers of various disciplines interested in any of our themes, meet each other, and know about each other’s work and research.

    WEEK THREE: COMPUTATION AND DEMOCRACY

     

    KEYNOTE SPEAKER: Jennifer Forestal, Assistant Professor of Political Science, Loyola University-Chicago

     

    DESIGNING FOR DEMOCRACY: HOW TO BUILD COMMUNITY IN DIGITAL ENVIRONMENTS

    From popping filter bubbles to dampening the spread of disinformation and extremism, scholars and practitioners alike have expended significant energy ‘fixing’ digital technologies—and the algorithms powering them—for democratic politics. But what, exactly, does democracy demand? In this talk, I outline the three “democratic affordances” required of digital technologies: they must 1) facilitate users’ recognitionthat, as members of communities, they share things with others, 2) generate users’ attachmentsto those communities and their fellow members, and 3) encourage experimentalismby empowering users to exert control over their environments. Drawing on resources from the history of political thought, architecture and urban design, social psychology, and media studies, I use the examples of platforms like Facebook, Twitter, and Reddit, as well as innovations like Gobo, to show how piecemeal solutions to the challenges posed by digital technologies are insufficient to meet the needs of democratic politics. Instead, I argue for a more holistic approach to building democratic spaces in these new digital environments.

    BIOGRAPHY

    Jennifer Forestal is the Helen Houlahan Rigali Assistant Professor of Political Science at Loyola University Chicago. She is a political theorist whose work focuses on practical democratic challenges, such as those introduced by digital technologies. She is the author of the forthcoming book Designing for Democracy: How to Build Community in Digital Environments(Oxford University Press) and co-editor of The Wives of Western Philosophy: Gender Politics in Intellectual Labor(Routledge, 2021). Her articles have appeared in peer-reviewed journals such as American Political Science Review, Journal of Politics, Political Studies, and Hypatia: A Journal of Feminist Philosophy.

     
    YOUNG RESEARCH SPEAKER: Aarushi Kalra, Ph.D. Candidate, Department of Economics, Brown University

     

    HATE SPEECH ON SOCIAL MEDIA IN INDIA

    In this project, we bring a novel dataset from ShareChat (a hugely popular content generation app in India) to launch an inquiry into the factors that lead to the production and propagation of online extreme speech, on platforms like WhatsApp. ShareChat, with more than 160 million monthly active users, is an Indian content generation platform where users can create, share, and engage with content in 14 non-English Indian regional languages. An important feature of the app is that a user’s account is linked to her WhatsApp account, so that content from ShareChat can be shared directly on WhatsApp. We wish to study the production and propagation of extreme speech against marginalized groups (like women and the Indian Muslim community) on WhatsApp, using ShareChat data. Given the immense popularity of WhatsApp and ShareChat, policy interventions that fight hate speech would help members of marginalized communities to access digital spaces where public opinion is formed.

    BIOGRAPHY

    Aarushi Kalra is a Ph.D. student in Economics at Brown. Her research interests include Public and Development Economics. Her current research deals with the causes and effects of communal tensions in India. Prior to Brown, she completed her MA in Economics at the Delhi School of Economics, worked at the Center for Development Economics, and taught Quantitative Methods at the Institute of Economic growth, all in New Delhi.

     

    For more information, please contact Shahrzad Haddadan. To see more of Fair February’s events and speakers, visit our main event page.

    This series is organized by Shahrzad Haddadan, Marie Schenk, and Cristina Menghini. Sponsored by the Data Science Initiative.

    See the full schedule here.

  • Join us on Wednesday, February 10th for a seminar series presentation, “Clinical Data Science: The Here and Now to Infinity and Beyond,” with Kristina Steinberg, MD, MMCi. From quality measures to predictive modeling, data science methods and techniques are helping to transform the US healthcare system. As a physician data scientist, Dr. Steinberg relies on her clinical knowledge when working with big data to unearth the stories and trends that lead to improved outcomes. In this presentation, Dr Steinberg will review topics and trends in data science that are currently being used with big healthcare data and explore the emerging topics and trends.

    Dr. Steinberg is a physician data scientist solving complex problems in the healthcare industry. Dr. Steinberg trained at top tier medical institutions, including Duke University, Yale University, & University of Texas Southwestern.

  • Collaborating across the globe is more critical to scientific progress than ever, and also easier than ever thanks to online tools. Join Torrey Truszkowski and Juliane Blyth from the Office of Research Integrity for a discussion about how to protect your data and ideas when shared internationally. We will also discuss how research data can range from non-restricted to highly restricted within the context of U.S. export control regulation, and what to look out for to ensure you and your collaborators do not run afoul of University policies and federal regulations.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Humanities, IRB, Libraries, ORI, OVPR, Research, Training, Professional Development
  • Researchers are often interested in extending (generalizing or transporting) findings to a target population of substantive interest. Examples include estimating how effective a treatment is or how well a prediction model performs when applied to a different population then was used for original treatment effect estimation or prediction model development. In this talk, I give a high level overview of evidence extension and provide an example of estimating how a lung cancer risk prediction model performs when deployed in a more racially diverse population than was used to develop the model.

  • Are you curious about how Data Use Agreements (DUAs) are being used to facilitate research on today’s hot topics? Jen Welch will discuss DUAs Brown has signed for data related to research on topics such as COVID-19, the opioid crisis, and racial disparities.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, IRB, ORI, OVPR, Research
  • Fair February: Data Science for Social Good

    Fair February is a three-week symposium organized by Brown’s Data Science Initiative. Each week of this symposium concentrates on a theme. The purpose of this symposium is to let young researchers of various disciplines interested in any of our themes, meet each other, and know about each other’s work and research.

    WEEK TWO: COMPUTATION AND WELFARE/POLICY-MAKING

     

    SESSION ONE SPEAKERS

     

    TOWARDS A SYSTEMATIC UNDERSTANDING OF EXEMPT AND NON-EXEMPT ALGORITHMIC BIASES

    Sanghamitra Dutta,  Ph.D. Candidate, Department Electrical and Computer Engineering, Carnegie Mellon University

    This talk is scheduled for 11:30am.

     

    AN AXIOMATIC THEORY OF PROVABLY-FAIR WELFARE-CENTRIC MACHINE LEARNING

    Cyrus Cousins,  Ph.D. Candidate, Department of Computer Science, Brown University

    This talk is scheduled for 12:10pm.

     

    SESSION TWO SPEAKERS

     

    (MACHINE) LEARNING WHAT POLICYMAKERS VALUE

    Samsun Knight,  Ph.D. Candidate, Department of Economics, Brown University

    This talk is scheduled for 2:05pm. 

     

    LET’S TALK CONNECTING ST&I STUDIES WITH DATA SCIENCE

    Mayra Morales Tirado,  Postdoctoral Research Associate, University of Manchester (UK)

    This talk is scheduled for 2:30pm. 

     

    FAIR COMPARISONS WITH OPTIMAL TRANSPORT

    Kweku Kwegyir-Aggrey,  Postdoctoral Research Associate, Department of Computer Science, Brown University

    This talk is scheduled for 3:00pm.

     

    For more information, please contact Shahrzad Haddadan. To see more of Fair February’s events and speakers, visit our main event page.

    This series is organized by Shahrzad Haddadan, Marie Schenk, and Cristina Menghini. Sponsored by the Data Science Initiative.

    See the full schedule here.

     

  • Fair February: Data Science for Social Good

    Fair February is a three-week symposium organized by Brown’s Data Science Initiative. Each week of this symposium concentrates on a theme. The purpose of this symposium is to let young researchers of various disciplines interested in any of our themes, to meet each other, and know about each other’s work and research.

    WEEK TWO: COMPUTATION AND WELFARE/POLICY-MAKING

     

    KEYNOTE SPEAKER: Daniel BjörkegrenAssistant Professor, Department of Economics, Brown University 

    MANIPULATION-PROOF MACHINE LEARNING

    An increasing number of decisions are guided by machine learning algorithms. In many settings, from consumer credit to criminal justice, those decisions are made by applying an estimator to data on an individual’s observed behavior. But when consequential decisions are encoded in rules, individuals may strategically alter their behavior to achieve desired outcomes. This paper develops a new class of estimators that is stable under manipulation, even when the decision rule is fully transparent. We explicitly model the costs of manipulating different behaviors and identify decision rules that are stable in equilibrium. Through a large field experiment in Kenya, we show that decision rules estimated with our strategy-robust method outperform those based on standard supervised learning approaches. 

    BIOGRAPHY

    Daniel Björkegren is an Assistant Professor of Economics at Brown University. He explores the opportunities generated by new technologies in the developing world and is working on methods to manage dominant networks and to make machine learning more humane. He holds a Ph.D. in Economics from Harvard University and a Bachelor’s degree in Physics from the University of Washington.

     

    YOUNG RESEARCH SPEAKER: Jessica Dai, Undergraduate Student, Department of Computer Science, Brown University

    FAIR MACHINE LEARNING UNDER PARTIAL COMPLIANCE

    Typically, fair machine learning research focuses on a single decision-maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision-makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does the strategic behavior of decision subjects in partial compliance settings affect the allocation outcomes? If k% of employers were to voluntarily adopt a fairness-promoting intervention, should we expect k% progress (in aggregate) towards the benefits of universal adoption, or will the dynamics of partial compliance wash out the hoped-for benefits? How might adopting a global (versus local) perspective impact the conclusions of an auditor? In this paper, we propose a simple model of an employment market, leveraging simulation as a tool to explore the impact of both interaction effects and incentive effects on outcomes and auditing metrics. Our key findings are that at equilibrium: (1) partial compliance (k% of employers) can result in far less than proportional (k%) progress towards the full compliance outcomes; (2) the gap is more severe when fair employers match global (vs local) statistics; (3) choices of local vs global statistics can paint dramatically different pictures of the performance vis-a-vis fairness desiderata of compliant versus non-compliant employers, and (4) partial compliance to local parity measures can induce extreme segregation.

    BIOGRAPHY

    Jessica Dai is a senior at Brown University studying computer science. She is broadly interested in (fair) machine learning and its limitations, especially when applied to the real world. She was recently a visiting student at Carnegie Mellon University advised by Professor Zachary Lipton and has also worked closely with Professor Sarah M. Brown from the University of Rhode Island (previously a DSI postdoc at Brown). In addition to research, she has been involved in developing the first iterations of Brown Computer Science’s Socially Responsible Computing undergraduate program.

     

    For more information, please contact Shahrzad Haddadan. To see more of Fair February’s events and speakers, visit our main event page.

    This series is organized by Shahrzad Haddadan, Marie Schenk, and Cristina Menghini. Sponsored by the Data Science Initiative.

    See the full schedule here.

  • Feb
    3
    3:00pm - 4:30pm

    Advance-CTR Introduction to REDCap

    Zoom

    Register now for our “Introduction to REDCap” virtual workshop with Sarah B. Andrea, PhD, MPH. Geared towards new or novice REDCap users, this class answers “What” REDCap is, “Why” you want to use it, and goes through the entire lifecycle of a REDCap project – from initial setup to data entry and finally exporting your data. The workshop will begin Wednesday February 3rd, 2020 at 3:00PM EST. 

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Entrepreneurship, Research, Training, Professional Development
  • Join us on Wednesday, February 3rd for a seminar series presentation, “A Call to Action: Implementing Telehealth in Community Based Primary Care as a Response to COVID-19,” with Care Transformation Collaborative Rhode Island (CTC-RI). The COVID-19 pandemic dramatically changed how consumers access care. CTC-RI launched the Primary Care Telehealth Needs Assessment in the Spring of 2020 to provide technical assistance to implement telehealth solutions to safely and effectively use telehealth to improve access to care and assist patients with management of chronic conditions.. This presentation will review the RFP, provide a market analysis of the needs of community based primary care practices, and solutions to aid community practices in the adoption of telehealth technologies.

    The Care Transformation Collaborative of Rhode Island/PCMH Kids is a multi-payer, patient centered medical home initiative promoting transformation in primary care in order to improve quality of care. This presentation will be given by Pano Yeracaris, MD MPH Chief Clinical Strategist, Sue Dettling, BS PCMH -CCE Telehealth Project Manager, & Susanne Campbell, RN MS PCMH-CCE Senior Project Director.

    Biology, Medicine, Public Health, Government, Public & International Affairs, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences
  • Fair February: Data Science for Social Good

    Fair February is a three-week symposium organized by Brown’s Data Science Initiative. Each week of this symposium concentrates on a theme. The purpose of this symposium is to let young researchers of various disciplines interested in any of our themes, meet each other, and know about each other’s work and research.

    WEEK ONE: COMPUTATION AND HEALTH

     

    FOCUSED QUERY EXPANSION WITH ENTITY CORES FOR PATIENT-CENTRIC HEALTH SEARCH

    Erisa Terolli, Postdoctoral Research Associate, Max Plank Institute

    This talk is scheduled for 11:30am.

     

     
    TOPOLOGICAL AND GEOMETRIC METHODS FOR COVID-19 TRACKING

    Ignacio Segovia-Dominguez,  Postdoctoral Research Associate and ConTex Fellow, University of Texas-Dallas

    This talk is scheduled for 12:10pm. 

     

    For more information, please contact Shahrzad Haddadan. To see more of Fair February’s events and speakers, visit our main event page.

    This series is organized by Shahrzad Haddadan, Marie Schenk, and Cristina Menghini. Sponsored by the Data Science Initiative.

    See the full schedule here.

     

  • Fair February: Data Science for Social Good

    Fair February is a three-week symposium organized by Brown’s Data Science Initiative. Each week of this symposium concentrates on a theme. The purpose of this symposium is to let young researchers of various disciplines interested in any of our themes, to meet each other, and know about each other’s work and research.

    WEEK ONE: COMPUTATION AND HEALTH
    KEYNOTE SPEAKER: Elizabeth Chen, Interim Director, BCBI, Brown University

     

    Artificial Intelligence in Healthcare

    Over the last five decades, artificial intelligence (AI) in medicine and health care has evolved along with advancements in data, technology, and computation. AI offers the potential to achieve the “quintuple aim” of enhancing patient and provider experiences, reducing costs, improving population health, and addressing equity and inclusion. However, there are a range of challenges in transforming electronic health data into clinically-actionable knowledge and implementing evidence-based innovations into practice. This keynote will begin with a history of AI in medicine followed by an overview of challenges and opportunities presented in the National Academy of Medicine’s 2019 special publication on “AI in Healthcare: The Hope, The Hype, The Promise, The Peril.” The talk will end with a discussion of the role of AI in local and national initiatives focused on COVID-19.

    Biography

    Elizabeth Chen , Ph.D., FACMI, is Interim Director of the Brown Center for Biomedical Informatics (BCBI), Associate Professor of Medical Science, Associate Professor of Health Services, Policy & Practice, Director of the Advance-CTR Biomedical Informatics and Cyberinfrastructure Enhancement Core, and Faculty Scholar in the Hassenfeld Child Health Innovation Institute at Brown University. Within BCBI, Dr. Chen leads the Clinical Informatics Innovation and Implementation (CI 3 ) Laboratory that is focused on leveraging Electronic Health Record (EHR) technology and data to improve healthcare delivery and biomedical discovery. Her current research projects involve the use of data, technology, and computational approaches for improving mental health ( mental health informatics ) and child health ( pediatric informatics ). Dr. Chen received a BS in Computer Science from Tufts University and Ph.D. in Biomedical Informatics from Columbia University. Prior to joining Brown University in July 2015, she held appointments at Columbia University, Partners HealthCare/Brigham and Women’s Hospital/Harvard Medical School, and the University of Vermont. Dr. Chen is currently Chair of the Biomedical Informatics, Library and Data Sciences (BILDS) Review Committee for the National Library of Medicine (NLM) at the National Institutes of Health (NIH), Associate Editor for Methods of Information in Medicine, and Editorial Board Member for the Journal of Biomedical Informatics.

     
    YOUNG RESEARCH SPEAKER: Ruotao Zhang, Ph.D. Candidate, Biostatistics, Brown University

     

    Identifying Subgroups with Differential Prediction Accuracy

    When reporting a prediction model’s performance, it is a standard practice only to report a measure of overall performance, i.e., how well the model predicts for the whole population it is evaluated on. However, it is also important for many applications to consider whether there is some sub-population that the model is more (less) effective at predicting, driving up (lowering) the overall prediction accuracy. In this talk, we discuss a tree-based algorithm for identifying subgroups with differential prediction accuracy. The algorithm is general in that it can accommodate any measure of model performance and any prediction model. We apply it to both simulated data and the National Lung Screening Trial (NLST) data. For the latter, we use non-imaging covariates (e.g., gender, age, race, smoking status) as inputs and identify subgroups with differential prediction performance under a previously developed lung cancer prediction model PLCOm2012. 

    Biography

    Ruotao is currently a Ph.D. candidate in the Department of Biostatistics under the supervision of Dr. Steingrimsson and Dr. Gatsonis. Before coming to the US, he worked as a data scientist at China Resources. Ruotao obtained an MSc in Applied Statistics from the University of Oxford, and a BSc in Mathematics from Imperial College London. His research interests mainly include statistical analysis of machine learning and deep learning models with applications to biomedical data.

     

    For more information, please contact Shahrzad Haddadan. To see more of Fair February’s events and speakers, visit our main event page

    This series is organized by Shahrzad Haddadan, Marie Schenk, and Cristina Menghini. Sponsored by the Data Science Initiative.

  • Fair February 2021
    Jan
    27

     

    FAIR FEBRUARY:

    DATA SCIENCE FOR SOCIAL GOOD 

    January 27 – February 12, 2021

    Fair February is a three-week series of talks presented by both keynote speakers and young researchers on topics at the intersection of social and computational sciences.

    Week 1: Computation and Health

    WednesdayKeynote Speaker: Elizabeth Chen, Brown Center for Biomedical Informatics (BCBI)

    FridayYoung Researchers’ Talks

    Week 2: Computation and Social Welfare

    WednesdayKeynote Speaker: Daniel Björkegren, Department of Economics, Brown University

    FridayYoung Researchers’ Talks

    Week 3: Computation and Democracy

    Wednesday -

    Keynote Speaker: Jennifer Forestal, Department of Political Science, Loyola University Chicago

    FridayYoung Researchers’ Talks

    Complete schedule here


    If you are interested, or for more information, please contact Shahrzad Haddadan. Organized by Shahrzad Haddadan, Marie Schenk, and Crisitina Menghini.

  • Join the Carney Institute for its first Brain Science External Postdoc Seminar Series (BrainExPo), featuring Sergey Stavisky, postdoctoral research fellow in the Neurosurgery Department of Stanford University. 

    Stavisky will discuss “Intracortical brain-computer interfaces: from fundamental science and engineering to restoring speech, reach and grasp.” 

    Abstract: Brain-computer interfaces (BCIs) are poised to profoundly transform human neuroscience and health by treating devastating – and currently incurable – nervous system injuries and diseases with precise, circuit-level measurements and interventions. BCIs can potentially restore the ability to speak, move, remember, and more. However, going from proof-of-concept studies in animal models to repairing or replacing patients’ damaged abilities requires a platform for understanding human-specific neural functions and designing, testing, and refining therapies in people. My strategy for accomplishing this is to develop advanced intracortical BCIs to restore reach & grasp movement and speech for people with paralysis. Motor BCI clinical trials can help individuals with severe speech and motor impairment in the near-term, and in doing so, validate the safety of new human-use devices capable of reading from and writing to thousands of neurons. These clinical trials also provide direct access to human neural circuits for gaining a deeper neuroscientific understanding of how the brain generates movements, which I believe will ultimately lead to better BCI therapies.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research
  • I2S2 Seminar: Computational Modeling for D&I -An Overview with Examples from the Field featuring Bo Kim, PhD.

    Dissemination and implementation (D&I) research focuses on strategies that are used to distribute and promote the uptake of evidence-based practices in health care settings. These settings are often complex systems that have multiple dependencies, competitions, relationships, and other interactions between their components and/or with their environments. To study these complexities, D&I researchers have begun to turn to computational modeling. This seminar session will discuss the relevance of computational modeling to D&I, and share examples of how computational modeling is being used by D&I studies (e.g., to enhance stakeholder engagement, to guide resource allocation). This session will additionally highlight several issues for consideration when using computational modeling to examine D&I, and propose future directions in which computational modeling can contribute to D&I research. As data-driven approaches to enhancing care remain central to learning health systems, this session will aim to serve as a forum on how D&I can harness computational modeling to support those systems’ implementation and sustained delivery of evidence-based practices.

    Headshot of Dr. Kim

     

    Dr. Kim is a mental health services researcher at the VA Center for Healthcare Organization and Implementation Research (CHOIR), and an Assistant Professor of Psychiatry at Harvard Medical School (HMS). With an academic background in systems science and engineering, her research interests are in applying multidisciplinary methodologies toward studying the quality and implementation of mental health services.

     

    I 2 S 2 covers the breadth of topics in effectively using data and technology to advance biomedical discovery and healthcare delivery. Each learning activity (seminar, journal club, workshop, or tutorial) features methods, applications, or resources that are aligned with components of a learning health system. This series is a joint initiative between the Brown Center for Biomedical Informatics , Brown Department of Psychiatry and Human Behavior Implementation Science Core , Rhode Island Quality Institute , and Advance Clinical and Translational Research (Advance-CTR) .

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences
  • Are you a student interested in pursuing a Certificate in Entrepreneurship? Drop in to learn more about the course requirements and curriculum for this exciting new opportunity offered by the Nelson Center for Entrepreneurship.

    Register here to attend.

    Advising, Mentorship, Education, Teaching, Instruction, Entrepreneurship, Teaching & Learning
  •  
    MATTHIAS STEINRÜECKEN

    Assistant Professor, Department of Ecology and Evolution, University of Chicago

     

    GENETIC HITCHHIKING IN POPULATIONS WITH VARIABLE SIZE

    Natural selection of beneficial or deleterious alleles results in an increase or decrease, respectively, of its frequency within the population. Due to chromosomal linkage, these dynamics affect the genetic variation at linked neutral loci, commonly referred to as genetic hitchhiking. Changes in population size, however, can yield patterns in observed genetic data that mimic the effects of selection. Thus, accurately modeling these dynamics is crucial to understanding how selection and past population size changes impact these patterns.

    We employ the Wright-Fisher diffusion, a mathematical framework describing the dynamics of haplotype frequencies, to study the impact of selection on linked neutral variation.

    In general, explicit solutions to this diffusion when selection and recombination act simultaneously is not known. Thus, we present a method for numerically evaluating the dynamics of the Wright-Fisher diffusion that can explicitly account for arbitrary population size histories. A key step in the method is to express the moments of the associated transition density as solutions (ODEs) to ordinary differential equations. Numerically solving these ODEs relies on a novel accurate and numerically efficient technique to estimate higher-order moments from those of lower order. This technique can also be applied more generally to the problem of extrapolating Site-Frequency-Spectra (SFS).

    We demonstrate how this numerical framework can be used to elucidate the reduction and recovery of genetic diversity around a selected locus over time and exhibit distortions in the SFS of neutral variation linked to loci under selection in various demographic settings.

    The method can be readily extended to more general modes of selection and has the potential to be applied in likelihood frameworks to detect loci under selection and infer the strength of the selective pressure.

     

    BIOGRAPHY

    Matthias Steinruecken is an Assistant Professor in the Department of Ecology and Evolution and runs the Steinruecken Lab at the University of Chicago. With their research, they develop computational and statistical methods for population genomics analysis to investigate the forces underlying genetic variation. Specifically, some things worked on include inferring demographic histories, admixture disease mapping, and inferring selection strength from ancient DNA or time series genetic data. 

     

    Data Wednesday Seminar Series

    Data Wednesday is the weekly seminar of the Data Science Initiative and the Center for Computational Molecular Biology. They are held most weeks during the academic year at 4 pm, in 164 Angell, 3rd floor (or via Zoom, while all talks are remote). Topics range from theoretical underpinnings of data science to domain-specific applications, including industry applications, and speakers include researchers from Brown as well as other institutions worldwide. If you would like to share your data-related work for Data Wednesday, please contact us. You can find all our events here or on our Events page.

     

    Subscribe to the DSI & CCMB Seminars Calendar and never miss a seminar announcement!

    View recent Data Wednesday talks here.

  • Please join us on January 20th for a seminar series presentation by Jennifer Joe, MD, “Has COVID-19 Pushed us into a New Era of Health Informatics Systems?”. Dr. Joe will discuss COVID-19 and it’s impact on health informatics systems. She will highlight the innovation acceleration we’ve seen in digital health, telemedicine, and AI/ML during the pandemic; review effective and ineffective solutions; and provide an assessment of a post-COVID ecosystem.

    Athletics, Sports, Wellness, Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research, Social Sciences
  • The Data Science Initiative will be holding an information session for undergraduates interested in the DSI UG Certificate in Data Fluency.

    For more information and courses, please visit the University Bulletin

     

    This virtual event will take place January 14, 2021 at 3:00 PM during the University’s Quiet Period.

  • The Advance-CTR Translational Research Seminar Series showcases clinical and translational research from across Rhode Island. Presentations, followed by feedback, allow presenters the opportunity to refine and strengthen their research. Seminars are held virtually on the second Thursday of each month.

    January

    • Sean Monaghan, MD: “Deep RNA Sequencing and Critical Care: Early Steps to Commercialization”
    • Jonghwan Lee, PhD: “Minimally-Invasive Retinal Prosthesis to Restore Vision in Blindness: Preclinical Study”

    Details: January 14, 2021 at 12 p.m. ET

    Advising, Mentorship, Biology, Medicine, Public Health, Education, Teaching, Instruction, Entrepreneurship, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research, Teaching & Learning, Training, Professional Development
  • Jan
    5
    9:00am - 10:30am

    Advance-CTR Introduction to REDCap

    Zoom

    Register now for our “Introduction to REDCap” virtual workshop with Sarah B. Andrea, PhD, MPH. Geared towards new or novice REDCap users, this class answers “What” REDCap is, “Why” you want to use it, and goes through the entire lifecycle of a REDCap project – from initial setup to data entry and finally exporting your data. The workshop will begin Tuesday January 5th, 2020 at 9:00AM EST.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Entrepreneurship, Research, Training, Professional Development
  • I2S2 Seminar: Rhode Island @ AMIA Year in Review

    Featuring presentations by:
    Katherine Brown, MSN, RN, PhD Student in the Brown Center for Biomedical Informatics (BCBI) and Center for Computational Molecular Biology (CCMB)
    Ryan Buckland, ScM, Data Scientist, Lifespan
    Aaron Eisman, MD/PhD Student in BCBI and CCMB
    Jiaying Lai, PhD Student in BCBI and CCMB

     

    AMIA (American Medical Informatics Association) is the professional home for biomedical informatics and hosts multiple meetings a year, including the Informatics Summit (March), Clinical Informatics Conference (May), and Annual Symposium (November). An introduction to AMIA and its meetings will be provided followed by highlights from the AMIA 2020 Virtual Annual Symposium. Studies that were accepted as student papers for this conference will also be presented: (1) Mental Health Comorbidity Analysis in Pediatric Patients with Autism Spectrum Disorder Using Rhode Island Medical Claims Data, (2) Selection of Clinical Text Features for Classifying Suicide Attempts, (3) Extracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures, and (4) A Phylogenetic Approach to Analyze the Conservativeness of BRCA1 and BRCA2 Mutations.

     

    I 2 S 2 covers the breadth of topics in effectively using data and technology to advance biomedical discovery and healthcare delivery. Each learning activity (seminar, journal club, workshop, or tutorial) features methods, applications, or resources that are aligned with components of a learning health system. This series is a joint initiative between the Brown Center for Biomedical Informatics , Brown Department of Psychiatry and Human Behavior Implementation Science Core , Rhode Island Quality Institute , and Advance Clinical and Translational Research (Advance-CTR) .

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Research, Social Sciences
  •  
    SRIRAM SANKARARAMAN

    Assistant Professor, Departments of Computer Science, Human Genetics, and Computational Medicine at UCLA

     

    HIGH-RESOLUTION POPULATION AND QUANTITATIVE GENETIC INFERENCE USING BIOBANK-SCALE STATISTICAL METHODS

    The quest to understand the interplay between evolution, genes, and traits has been revolutionized by the collection of rich phenotypic and genetic data across hundreds of thousands of individuals in diverse populations.

    This talk will describe how we bring together statistical and computational insights to design accurate and highly scalable algorithms for a suite of inference problems ranging from estimating fine-scale population structure to dissecting the genetic and novel insights into genetic loci under recent positive selection, how genetic effects are distributed across the genome, and the relative contributions of additive, dominance, and gene-environment interaction effects to trait variation.

     
    BIOGRAPHY

    Sriram Sankararaman is an Assistant Professor in the Departments of Computer Science, Human Genetics, and Computational Medicine at UCLA. His research interests lie at the interface of computer science, statistics, and biology. His lab develops machine learning algorithms to analyze genomic data and clinical data with the broad goal of understanding the interplay between evolution, genomes, and traits. He received his undergraduate degree in Computer Science from UC Berkeley and was a postdoctoral fellow at Harvard Medical School before joining UCLA. He is a recipient of a National Science Foundation Career Award, and of fellowships from Microsoft Research, the Sloan Foundation, the Okawa Foundation, and the Simons Institute.

     

    Data Wednesday Seminar Series

    Data Wednesday is the weekly seminar of the Data Science Initiative and the Center for Computational Molecular Biology. They are held most weeks during the academic year at 4 pm, in 164 Angell, 3rd floor (or via Zoom, while all talks are remote). Topics range from theoretical underpinnings of data science to domain-specific applications, including industry applications, and speakers include researchers from Brown as well as other institutions worldwide. If you would like to share your data-related work for Data Wednesday, please contact us. You can find all our events here or on our Events page.

     

    Subscribe to the DSI & CCMB Seminars Calendar and never miss a seminar announcement!

     

  • Join Carney’s Center for Computational Brain Science (CCBS) for a seminar on “Neural reinforcement: re-entering and refining neural dynamics leading to desirable outcomes.” This event will feature Vivek Athalye, Ph.D., postdoctoral researcher at Columbia University.

    Abstract:
    How do organisms learn to do again, on-demand, a behavior that led to a desirable outcome? Dopamine-dependent cortico-striatal plasticity provides a framework for learning behavior’s value, but it is less clear how it enables the brain to re-enter desired behaviors and refine them over time. Reinforcing behavior is achieved by re-entering and refining the neural patterns that produce it. We review studies using brain-machine interfaces which reveal that reinforcing cortical population activity requires cortico-basal ganglia circuits. Then, we propose a formal framework for how reinforcement in cortico-basal ganglia circuits acts on the neural dynamics of cortical populations. We propose two parallel mechanisms: i) fast reinforcement which selects the inputs that permit the re-entrance of the particular cortical population dynamics which naturally produced the desired behavior, and ii) slower reinforcement which leads to refinement of cortical population dynamics and more reliable production of neural trajectories driving skillful behavior on-demand.
    CCBS, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research
  • Desmond Upton Patton, PhD
    Associate Professor of Social Work; Associate Dean of Curriculum Innovation and Academic Affairs
    Columbia University School of Social Work

    Desmond Upton Patton’s research uses qualitative and computational data collection methods to examine the relationship between youth and gang violence and social media; how and why violence, grief, and identity are expressed on social media; and the real-world impact these expressions have on well-being for low-income youth of color. He studies the ways in which gang-involved youth conceptualize threats on social media, and the extent to which social media shapes and facilitates youth and gang violence.

    The Decoding Disparities Lecture Series

    The Decoding Disparities Lecture Series is sponsored by The Warren Alpert Medical School of Brown University and the Brown School of Public Health to examine health inequity and to outline steps toward a more equitable and just health care system.

    The series is supported by The Paul Levinger Professorship Pro Tem in the Economics of Health Care. This lecture was established in 1987 to honor the memory of Paul Levinger by a gift from his wife, Ruth N. Levinger, on behalf of the Levinger family. The Levingers’ daughter and son-in-law, Bette Levinger Cohen and John M. Cohen ’59, MD were instrumental in Mrs. Levinger’s decision to make this gift.

    Continuing Medical Education Credit

    This live activity is approved for AMA PRA Category 1 Credit

    Physicians: To be eligible to claim CME credit, please register for this event at cme-learning.brown.edu

    decodingdisparities
  •  
    MARTA GONZALEZ

    Associate Professor of City and Regional Planning, UC Berkeley

     

    CASES OF STUDY IN COMPUTATIONAL URBAN SCIENCE

    Computational Urban Sciences refer to the use of information and communication technology and data in the context of cities and urban environments. First, I present methods to identify patterns of behavior in energy consumption and credit card transactions. Then I show how this is a more complex task when working with environmental data. I finalize with open questions and proposed research to study human-natural systems interactions. 

     
    BIOGRAPHY

    Marta C. Gonzalez is an Associate Professor of City and Regional Planning and Civil and Env. Eng. at UC Berkeley, and a Physics Research faculty at the Lawrence Berkeley National Laboratory (Berkeley Lab). With support from several companies, cities, and foundations from around the world, her research team develops computational models to analyze digital traces to estimate the demand for urban infrastructures in relation to energy and mobility.

    Examples are traffic gridlocks and the integration of electric vehicles in the power grid, policy of solar energy adoption, and habits in spending behavior. Her research has been published in leading journals, including Science, PNAS, Nature and Physical Review Letters.

     

    Data Wednesday Seminar Series

    Data Wednesday is the weekly seminar of the Data Science Initiative and the Center for Computational Molecular Biology. They are held most weeks during the academic year at 4 pm, in 164 Angell, 3rd floor (or via Zoom, while all talks are remote). Topics range from theoretical underpinnings of data science to domain-specific applications, including industry applications, and speakers include researchers from Brown as well as other institutions worldwide. If you would like to share your data-related work for Data Wednesday, please contact us. You can find all our events here or on our Events page.

     

    Subscribe to the DSI & CCMB Seminars Calendar and never miss a seminar announcement!

     

  • Advance-CTR Pilot Project awardee, Dr. Tao, and Mentored Research Scholar, Dr. Samuels, share their research:

    • Jun Tao, PhD: “Using Big Data to Determine Pre-exposure Prophylaxis Uptake and Persistence in Southern New England
    • Elizabeth Samuels, MD: “Identifying Opioid Overdose Hotspots for Prevention and Treatment Resource Deployment

    Register now!

    Biology, Medicine, Public Health, Research, Training, Professional Development
  •  
    FERNANDO VILLANEA

    Brown University Center for Computational Molecular Biology

     

    ARCHAIC INTROGRESSION IN MODERN HUMANS: WHAT ARCHAIC GENOME FRAGMENTS CAN TELL US ABOUT PREHISTORY AND MODERN HEALTH

    As anatomically modern human populations began their latest expansion outside of Africa around 70,000 years ago, they encountered other archaic humans – Neanderthals and Denisovans – and their interactions left a lasting impact on modern human genomes. Here, I discuss two general applications of studying the archaic genetic data which survives in our genomes. The first is using this information to understand the past population dynamics of archaic species – in this case, to model the admixture event between Neanderthals and humans. The second application is looking at how modern humans have co-opted archaic gene variants to their advantage – in this case, observing the frequency of the Denisovan version of the gene MUC19 in modern populations. These two projects (and others) are the culmination of two years of research at the Huerta-Sanchez lab at Brown’s CCMB.

     
    BIOGRAPHY

    I am interested in understanding the genetic legacy of Neanderthals and other archaic human species. My work is focused on learning about the natural history of archaic species as observed through genetic data; including the ancient genomes sequenced from individuals long dead, as well as the small fragments of archaic DNA inherited in people living today. My favorite theoretical frame is the coalescent, my favorite method is computer simulation, and my favorite analysis tool is Approximate Bayesian Computation.

     

     

    Data Wednesday Seminar Series

    Data Wednesday is the weekly seminar of the Data Science Initiative and the Center for Computational Molecular Biology. They are held most weeks during the academic year at 4 pm, in 164 Angell, 3rd floor (or via Zoom, while all talks are remote). Topics range from theoretical underpinnings of data science to domain-specific applications, including industry applications, and speakers include researchers from Brown as well as other institutions worldwide. If you would like to share your data-related work for Data Wednesday, please contact us. You can find all our events here or on our Events page.

     

    Subscribe to the DSI & CCMB Seminars Calendar and never miss a seminar announcement!

  • Dec
    9
    12:00pm - 1:30pm

    Introduction to REDCap

    Zoom

    Workshop Description
    Geared toward new or novice REDCap users, “Introduction to REDCap” answers “what” REDCap is, “why” you want to use it, and goes through the entire life cycle of a REDCap project – from initial setup to data entry and finally exporting your data.

    About the Instructor
    Sarah B. Andrea, PhD, MPH, is a research scientist with the Lifespan Biostatistics Core at Rhode Island Hospital. In addition to providing design and statistical oversight and mentorship, she also conducts research investigating strategies to mitigate race-, class-, and gender-based inequities in health throughout the life course. Dr. Andrea has been working with REDCap for five years.

    Register now!

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Kip Thorne
    Dec
    2
    6:30pm

    A Virtual ICERM Public Event: Q&A with Kip Thorne, Nobel Prize-winning Theoretical Physicist

    Via Zoom (credentials will be sent to all registered participants the day before and day of event)

    Please join us for an exciting Q&A with Nobel prize-winning physicist Kip Thorne. Professor Thorne will briefly review the crucial role and history of computation in the detection of gravitational waves, and take your questions on all issues relating to computational physics and science in general.

    The event will be introduced and moderated by renowned physicist Professor Richard Price, and Professor Saul Teukolsky (the 2021 Einstein Prize awardee) will give an introductory talk on the computational challenges and solutions for simulating black holes and gravitational waves on computers, and the interesting science that can be done thanks to the LIGO and VIRGO gravitational-wave detectors.

    ABOUT THE SPEAKERS
    Kip Thorne is a theoretical physicist known for his contributions in gravitational physics and astrophysics. A longtime friend and colleague of Stephen Hawking and Carl Sagan, he was the Feynman Professor of Theoretical Physics at the California Institute of Technology (Caltech) until 2009 and is one of the world’s leading experts on the astrophysical implications of Einstein’s general theory of relativity. He continues to do scientific research and scientific consulting, most notably for the Christopher Nolan film Interstellar. Thorne was awarded the 2017 Nobel Prize in Physics along with Rainer Weiss and Barry C. Barish “for decisive contributions to the LIGO detector and the observation of gravitational waves”.

    Richard Price earned his Ph.D. from Caltech under the supervision of Kip Thorne. From there, he spent part of his career at the University of Utah, where he holds the title of Emeritus Professor. In 2004 he joined the Center for Gravitational Wave Astronomy at the University of Texas, and became Senior Lecturer in physics at MIT in 2015. He is also on the adjunct faculty at the University of Massachusetts. In 2017, Price became the editor of the American Journal of Physics. Subsequent work has provided a major impetus for the development of gravitational wave detectors such as LIGO. He is a Fellow of the American Physical Society and the American Association for the Advancement of Science.

    Saul Teukolksy earned his Ph.D. from Caltech under the supervision of Kip Thorne. Since then he has held a faculty position at Cornell University, where he is currently the Hans A. Bethe Professor of Physics. His earliest work led to the development of an equation that describes how a black hole interacts with surrounding objects. Subsequent research has included the physics of pulsars and supernova explosions, properties of rapidly rotating neutron stars, stellar dynamics, and planets around pulsars. His current project uses supercomputers to study colliding black holes as part of a world-wide effort trying to solve Einstein’s equations of general relativity. Recently, one of his group’s wave forms was used to compare theory with experiment in the first detection by the Laser Interferometer Gravitational Wave Observatory (LIGO). He is a Fellow of the American Physical Society and a Fellow of the American Astronomical Society. He was elected to the American Association for the Advancement of Science and the National Academy of Sciences and was most recently awarded the Einstein Prize.

    Zoom credentials will be sent to all registered participants the day before and the day of the event.

    Black Holes, computational physics, gravitational waves, LIGO
  • BRENDEN LAKE

    Assistant Professor of Psychology and Data Science, NYU

    LEARNING THROUGH THE EYES OF A CHILD

    Young children have meaningful expectations about the world around them. What is the origin of this early knowledge? How much can be explained through generic learning mechanisms applied to sensory data, and how much requires more substantive innate inductive biases? Addressing this fundamental question in its full generality is infeasible, but we can hope to make real progress in more narrowly defined domains, such as the development of high-level visual categories, thanks to new datasets and progress in deep learning. We train large-scale neural networks through the eyes of a single developing child, using longitudinal baby headcam videos (Sullivan et al., 2020, PsyArxiv). Our results show how high-level visual representations emerge from a subset of one baby’s experience, through only self-supervised learning.

     
    Biography

    Brenden builds computational models of everyday cognitive abilities, focusing on problems that are easier for people than they are for machines. The human mind is the best-known solution to a diverse array of difficult computational problems: learning new concepts, learning new tasks, understanding scenes, learning a language, asking questions, forming explanations, amongst many others. Machines also struggle to simulate other facets of human intelligence, including creativity, curiosity, self-assessment, and commonsense reasoning.

    In this broad space of computational challenges, Brenden’s work has addressed a range of questions: How do people learn a new concept from just one or a few examples? How do people act creatively when designing new concepts? How do people learn qualitatively different forms of structure? How do people ask questions when searching for information?

    By studying these distinctively human endeavors, there is potential to advance both cognitive science and machine learning. In cognitive science, building a computational model is a test of understanding; if people outperform all existing algorithms on certain types of problems, we have more to understand about how people solve them. In machine learning, these cognitive abilities are both important open problems as well as opportunities to reverse engineer human solutions. By studying human solutions to difficult computational problems, Brenden aims to better understand humans and to build machines that learn in more powerful and more human-like ways.

     

    Follow Brenden on Twitter: @LakeBrenden

     

    DSI & CCMB Data Wednesday Seminar Series

    The Data Science Initiative (DSI) joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science. Please check our events page for more information on these and other events of interest.

  • XIHONG LIN

    Professor and Former Chair, Department of Biostatistics

    Coordinating Director of the Program in Quantitative Genomics at the Harvard T. H. Chan School of Public Health

    Professor of the Department of Statistics at the Faculty of Arts and Sciences of Harvard University

    Associate Member of the Broad Institute of Harvard and MIT

     

    LEARNING FROM COVID-19 DATA IN WUHAN, USA, AND THE WORLD ON TRANSMISSION, HEALTH OUTCOMES, AND INTERVENTIONS

    COVID-19 is an emerging respiratory infectious disease that has become a pandemic. In this talk, I will first provide a historical overview of the epidemic in Wuhan. I will then provide the analysis results of 32,000 lab-confirmed COVID-19 cases in Wuhan to estimate the transmission rates using Poisson Partial Differential Equation based transmission dynamic models. This model is also used to evaluate the effects of different public health interventions on controlling the COVID-19 outbreak, such as social distancing, isolation, and quarantine. I will present the results of the epidemiological characteristics of the cases. The results show that multi-faceted intervention measures successfully controlled the outbreak in Wuhan. I will next present transmission regression models for estimating transmission rates in the USA and other countries, as well as factors including intervention effects using social distancing, test-trace-isolate strategies that affect transmission rates. I will present the analysis results of >500,000 participants of the HowWeFeel project on symptoms and health conditions in the US, and discuss the risk factors of the epidemic. I will discuss the estimation of the proportion of undetected cases, including asymptomatic, pre-symptomatic cases, and mildly symptomatic cases, the chances of a resurgence in different scenarios, and the factors that affect transmissions. I will provide several takeaways and discuss priorities.

     
    Biography:

    Dr. Lin is an elected member of the National Academy of Medicine. She received the 2002 Mortimer Spiegelman Award from the American Public Health Association and the 2006 Committee of Presidents of Statistical Societies (COPSS) Presidents’ Award and the 2017 COPSS FN David Award. She is an elected fellow of the American Statistical Association (ASA), Institute of Mathematical Statistics, and International Statistical Institute.

    Dr. Lin’s research interests lie in the development and application of statistical and computational methods for the analysis of massive data from the genome, exposome, and phenome, and scalable statistical inference and learning for big genomic, epidemiological, and health data. Examples include analytic methods and applications for large scale Whole Genome Sequencing studies, biobanks and electronic health records, whole-genome variant functional annotations, genes and environment, multiple phenotype analysis, risk prediction, integrative analysis of different types of data, causal mediation analysis and causal inference, analysis of epidemiological and complex observational study data. Her theoretical and computational statistical research includes statistical methods for testing a large number of complex hypotheses, causal inference, statistical inference for large covariance matrices, prediction models using high-dimensional data, cloud-based statistical computing, and statistical methods for epidemiological studies.

    Dr. Lin’s statistical methodological research has been supported by the MERIT Award (R37) (2007-2015) and the Outstanding Investigator Award (OIA) (R35) (2015-2022) from the National Cancer Institute (NCI). She is the contact PI of the Harvard Analysis Center of the Genome Sequencing Program of the National Human Genome Research Institute, and the multiple PI of the U19 grant on Integrative Analysis of Lung Cancer Etiology and Risk from NCI. She is also the contact PI of the T32 training grant on interdisciplinary training in statistical genetics and computational biology. She is the former contact PI of the Program Project (PO1) on Statistical Informatics in Cancer Research from NCI.

    Dr. Lin is the former Chair of the COPSS (2010-2012) and a former member of the Committee of Applied and Theoretical Statistics (CATS) of the National Academy of Science. She co-launched the new Section of Statistical Genetics and Genomics of the American Statistical Association and served as a former section chair. She is the former Coordinating Editor of Biometrics and the founding co-editor of Statistics in Biosciences. She has served on a large number of committees of many statistical societies, and numerous NIH and NSF review panels.

  • Join the Carney Institute for Brain Science for a conversation focused on Alzheimer’s research at Brown University, featuring:

    • Stephen Salloway, Martin M. Zucker Professor of Psychiatry and Human Behavior at Brown, director of neurology and the Memory and Aging Program at Butler Hospital in Providence, RI
    • Ashley Webb, Richard and Edna Salomon Assistant Professor of Molecular Biology, Cell Biology and Biochemistry

    This event will be moderated by Diane Lipscombe, Reliance Dhirubhai Ambani Director of the Carney Institute, and Christopher Moore, associate director of the Carney Institute.

    ALZ, Biology, Medicine, Public Health, CTN, Psychology & Cognitive Sciences, Research
  • Terrance Savitsky, PhD

    Research Mathematical Statistician

    Mathematical Statistics Research Center

    U. S. Bureau of Labor Statistics

    Title:  Pseudo Posterior Mechanism under Differential Privacy

    Abstract:  We propose a Bayesian pseudo posterior mechanism to generate record-level synthetic datasets equipped with a differential privacy (DP) guarantee from any proposed synthesis model. The pseudo posterior mechanism employs a data record-indexed, risk-based weight vector with weights ∈ [0, 1] to surgically downweight high-risk records for the generation and release of record-level synthetic data. The differentially private pseudo posterior synthesizer constructs weights using Lipschitz bounds for a log-pseudo likelihood utility for each data record, which provides a practical, general formulation for using weights based on record-level sensitivities that we show achieves dramatic improvements in the DP expenditure as compared to the unweighted posterior mechanism. By selecting weights to remove likelihood contributions with non-finite log-likelihood values, we achieve a local privacy guarantee at every sample size. We compute a local sensitivity specific to our Consumer Expenditure Surveys dataset for family income, published by the U.S. Bureau of Labor Statistics, and reveal mild conditions that guarantee its contraction to a global sensitivity result over the space of databases. We further employ a censoring mechanism to lock-in a local result with desirable risk and utility performances to achieve a global privacy result as an alternative to relying on asymptotics. We show that utility is better preserved for our pseudo posterior mechanism as compared to the exponential mechanism (EM) estimated on the same non-private synthesizer due to the use of targeted downweighting. Our results may be applied to any synthesizing model envisioned by the data disseminator in a computationally tractable way that only involves estimation of a pseudo posterior distribution for parameter(s) θ, unlike recent approaches that use naturally-bounded utility functions under application of the EM.   

    (Joint work with Matthew R. Williams and Jingchen Hu)


    Keywords: Differential privacy, Pseudo posterior, Pseudo posterior mechanism, Synthetic data

    For more information about the Statistics Seminar Series go here.

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • PLEASE NOTE THIS TALK IS FOR FACULTY ONLY.  

     

    THIS TALK HAS BEEN RESCHEDULED TO BEGIN AT 1:00 PM.

     

    BRENDA RUBENSTEIN

    Joukowsky Family Assistant Professor of Chemistry, Brown University

    LEARNING QUANTUM MECHANICS: HOW MACHINE LEARNING IS (AND IS NOT) TRANSFORMING THE PRACTICE OF QUANTUM MECHANICS

    Much of our ability to understand the quantum world, including how to design new materials, synthesize new molecules, and study puzzling emergent quantum phases, rests upon our ability to accurately solve the Schrodinger Equation. While the recipe for solving the Schrodinger Equation has been known for over a century, the cost of finding its exact solutions scales exponentially with system size, a fact which has frustrated the progress of quantum chemistry and physics for decades. The rise of data science, however, presents new and exciting opportunities for potentially accelerating the solution of the Schrodinger Equation - or foregoing its solution whatsoever by directly predicting quantum properties. In this talk, I will describe how machine learning is and isn’t transforming our ability to model quantum phenomena drawing upon examples from my own group’s research and the wider literature.

     
    Biography:

    Dr. Brenda Rubenstein is currently the Joukowsky Family Assistant Professor of Chemistry at Brown University. While the focus of her work is on developing new electronic structure methods, she is also deeply engaged in rethinking computing architectures. Prior to arriving at Brown, she was a Lawrence Distinguished Postdoctoral Fellow at Lawrence Livermore National Laboratory. She received her Sc.B.s in Chemical Physics and Applied Mathematics at Brown University, her M.Phil. in Computational Chemistry while a Churchill Scholar at the University of Cambridge, and her Ph.D. in Chemical Physics at Columbia University. Ask her about basketball - you may be surprised!

     

    Faculty for Faculty Research Talks

    This is an opportunity for faculty to share current data science-related research activities with other faculty colleagues in an informal and interdisciplinary environment. More about this series on our website.

  • Use of the OpenMRS open source EHR to support management of COVID-19 in Haiti, Nepal and Kenya

    Hamish Fraser MBChB, MSc FACMI, Associate Professor of Medical Science, Center for Biomedical Informatics, Brown University
    Steven Wanyee MSc, Director of Biomedical Informatics, IntelliSOFT Consulting Limited, Nairobi, Kenya
    Managing COVID-19 requires accurate up to date data on new cases, contact tracing and case management. Managing epidemic diseases is a core function of health systems in many low and middle income countries, such as recent outbreaks of Ebola in West Africa, and Dengue, Zika and cholera in many countries. This has spurred the development of health information systems to capture data in community and health facility settings and to report data to public health authorities and contact tracing teams. Several established open source health information systems have been deployed to support management of COVID-19 including mobile health platforms (CommCare, MedicMobile), a national aggregate data reporting system (DHIS2) and a surveillance system (SORMAS). In this presentation I will describe the use of the OpenMRS open source EHR system in management of COVID-19 on Haiti and Nepal. Mr Wanyee will connect from Kenya to describe a large scale implementation of OpenMRS for the MOH national COVID-19 data management system there. We will also discuss the role of primary care EHR systems in improving disease surveillance.
    I 2 S 2 covers the breadth of topics in effectively using data and technology to advance biomedical discovery and healthcare delivery. Each learning activity (seminar, journal club, workshop, or tutorial) features methods, applications, or resources that are aligned with components of a learning health system. This series is a joint initiative between the Brown Center for Biomedical Informatics , Brown Department of Psychiatry and Human Behavior Implementation Science Core , Rhode Island Quality Institute , and Advance Clinical and Translational Research (Advance-CTR) .
    Biology, Medicine, Public Health, Government, Public & International Affairs, International, Global Engagement, Research, Social Sciences
  • Carney Methods Meetups: Beyond the Brady Bunch Meeting

    Join the Carney Institute for Brain Science for a Carney Methods Meetups, an informal gathering focused on methods for brain science, on Thursday, November 19, at 2 p.m.

    Jason Ritt, Carney’s scientific director of quantitative neuroscience, David Sheinberg, professor of neuroscience, and Andrew Creamer, scientific data management specialist in the Brown Library, will lead an open discussion of tools and tricks to enhance virtual meetings for brainstorming, collaborative manuscript editing, poster presentation, social events, and others. Some tools like OBS studio and spatial.chat will be briefly demonstrated, and we invite further ideas from the community.

    Please note, this workshop requires you to be logged into Zoom through your Brown account. Click to learn more.

    Notes from previous Meetups are available online.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research, Teaching & Learning
  • Join the Brown Arts Initiative and Data Science Initiative for a series of conversations at the intersection of data science and design with a focus on social justice. In the words of Ramon Tejada, founder of the Decolonizing Design Reader, “The histories of these fields, with their respective exclusionary practices and methods, demonstrate complicity in creating the societal problem we face today. Many of these issues raise uncomfortable concepts that we all need to work through in our own individual practices as scientists, designers, and human beings.” Acknowledging moments of convergence, these interdisciplinary talks seek to generate new collaborations at Brown and beyond while acknowledging that data and design are not neutral.

    November 18, 12 PM: Radical Prescience: Silas Munro on W.E.B DuBois’s Data Portraits

    Silas Munro - founder of Polymode design studio and an Associate Professor at Otis College of Art and Design - revisits his tour de force lecture on W.E.B. DuBois’s groundbreaking data visualizations following the recent anti-racism uprisings and the increasing visibility and support of the Black Lives Matter movement.

    November 19, 12 PM: Empowerment Over Oppression: Design and Data for Social Justice

    Join Alice Grandoit (co-founder of Deem Journal, a new biannual print and online platform focused on design as social practice) and Sarah Williams (author of the forthcoming Data Action: Using Data for Public Good) in a conversation moderated by Ramon Tejada (founder of the Decolonizing Design Reader).

     

    Alice Grandoitis a social engagement designer and cultural researcher building awareness through strategic community partnerships, programming, and experiences. Her practice is rooted in empowerment, cultural collaborations, and the creation of experiential platforms molded around emergent creatives. These ideas are united by her work as the co-founder and editorial director of Deem Journal.

    Silas Munrois a partner of Polymode, a bi-coastal design studio, creating poetic, and research-informed design with clients in the cultural sphere and community-based organizations. Clients of Polymode include MoMA, The Phillips Collection, Mark Bradford at the Venice Biennale, The Center for Urban Pedagogy, Walker Art Center, Cooper Hewitt Design Museum, ICA at Virginia Commonwealth University, and The New Museum. Munro also serves as Associate Professor of Communication Arts at the Otis College of Art and Design in Los Angeles, and Advisor, Founding Faculty, and Chair Emeritus at Vermont College of Fine Arts. In the past year he has emerged as one of the most exciting practitioners of community-engaged design and as an influential scholar known for his contributions to W. E. B. Du Bois’s Data Portraits: Visualizing Black Americapublished by Princeton Architectural Press in late 2018. In workshops and lectures he addresses post-colonial relationships between design and marginalized communities and offers practical ways for educators and practitioners to decolonize the way design is taught and to create inclusive new frameworks. His design and writing has been published in books, exhibitions, and websites in Germany, Japan, Korea, the US, and the UK including Chronicle Books, IDEA magazine, Eye, and Slanted magazine. Munro earned a BFA from Rhode Island School of Design and an MFA from California Institute of the Arts.

    Ramon Tejada is a (New Yorkino / American) designer ( as Estudio Ramon ) and educator based in Providence, RI. He works in a hybrid design/teaching practice focusing on collaboration, inclusion, unearthing and the responsible expansion of design – a practice he has named “puncturing.” Ramon is an Assistant Professor in the Graphic Design Department at RISD.

    Sarah Williamsis currently an Associate Professor of Technology and Urban Planning at the Massachusetts Institute of Technology (MIT) where she also directs the Civic Data Design Lab and chairs MIT’s new undergraduate program in Urban Science. Williams combines her training in computation and design to create communication strategies that expose urban policy issues to broad audiences and create civic change. She calls the process Data Action, which is also the name of her recent book published by MIT Press. Williams is co-founder and developer of Envelope.city, a web-based software product that visualizes and allows users to modify zoning in New York City. Before coming to MIT, Williams was Co-Director of the Spatial Information Design Lab at Columbia University’s Graduate School of Architecture Planning and Preservation (GSAPP), where she was a member of the Million Dollar Blocks team which is well known for using visualization to highlight the costs of incarceration. Her design work has been widely exhibited including work in the Guggenheim, the Museum of Modern Art (MoMA), Venice Biennale, and the Cooper Hewitt Museum. Williams has won numerous awards including being named top 25 planners in the technology and Game Changer by Metropolis Magazine. Check out her upcoming exhibition, Visualizing NYC 2021, at the Center for Architecture in New York City which opens November 17th.

  • Please join Carney’s Center for Computational Brain Science (CCBS) on November 18 for a special seminar on “Differential Resilience of Neurons and Networks with Similar Behavior to Perturbation,” featuring Eve Marder, Ph.D., university professor and Victor and Gwendolyn Beinfield Professor of Biology at Brandeis University.

    Please note, you must be logged into Zoom through your Brown account to join this event. 

    Abstract:

    Both computational and experimental results in single neurons and small networks demonstrate that very similar network function can result from quite disparate sets of neuronal and network parameters. Using the crustacean stomatogastric nervous system, we study the influence of these differences in underlying structure on differential resilience of individuals to a variety of environmental perturbations, including changes in temperature, pH, potassium concentration and neuromodulation. We show that neurons with many different kinds of ion channels can smoothly move through different mechanisms in generating their activity patterns, thus extending their dynamic range.

    Biology, Medicine, Public Health, CCBS, Psychology & Cognitive Sciences, Research
  • NANDITA GARUD

    Assistant Professor, Ecology and Evolutionary Biology, UCLA

    RAPID ADAPTATION IN NATURAL POPULATIONS:  LESSONS FROM DROSOPHILA AND THE HUMAN MICROBIOME

    The availability of whole-genome data from natural populations has challenged many long-standing assumptions about molecular evolution. For example, it has long been assumed that natural selection is typically slow and infrequent. Using whole-genome data from both Drosophila and the human microbiome, I found evidence that rapid adaptation is much more pervasive than previously thought. In my talk, I will first describe a method I developed to detect soft sweeps, a signature of rapid adaptation, and its application to Drosophila and other, non-model organism data. Next, I will show that selective sweeps of genes and SNPs in bacteria in the human microbiome are common on 6-month time scales and that these sweeps likely originate in adaptive introgression from other species and strains in the microbiome. This suggests that complex ecological communities can play an important role in shaping evolution on short time scales. In sum, I will describe how we can leverage whole-genome data and novel statistics for uncovering the mode and tempo of adaptation in natural populations.

    DSI & CCMB Data Wednesday Seminar Series

    The Data Science Initiative (DSI) joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science. Please check our events page for more information on these and other events of interest.

  • Join the Brown Arts Initiative and Data Science Initiative for a series of conversations at the intersection of data science and design with a focus on social justice. In the words of Ramon Tejada, founder of the Decolonizing Design Reader, “The histories of these fields, with their respective exclusionary practices and methods, demonstrate complicity in creating the societal problem we face today. Many of these issues raise uncomfortable concepts that we all need to work through in our own individual practices as scientists, designers, and human beings.” Acknowledging moments of convergence, these interdisciplinary talks seek to generate new collaborations at Brown and beyond while acknowledging that data and design are not neutral.

    November 18, 12 PM: Radical Prescience: Silas Munro on W.E.B DuBois’s Data Portraits

    Silas Munro - founder of Polymode design studio and an Associate Professor at Otis College of Art and Design - revisits his tour de force lecture on W.E.B. DuBois’s groundbreaking data visualizations following the recent anti-racism uprisings and the increasing visibility and support of the Black Lives Matter movement.

    November 19, 12 PM: Empowerment Over Oppression: Design and Data for Social Justice

    Join Alice Grandoit (co-founder of Deem Journal, a new biannual print and online platform focused on design as social practice) and Sarah Williams (author of the forthcoming Data Action: Using Data for Public Good) in a conversation moderated by Ramon Tejada (founder of the Decolonizing Design Reader).

    Alice Grandoitis a social engagement designer and cultural researcher building awareness through strategic community partnerships, programming, and experiences. Her practice is rooted in empowerment, cultural collaborations, and the creation of experiential platforms molded around emergent creatives. These ideas are united by her work as the co-founder and editorial director of Deem Journal.

    Silas Munrois a partner of Polymode, a bi-coastal design studio, creating poetic, and research-informed design with clients in the cultural sphere and community-based organizations. Clients of Polymode include MoMA, The Phillips Collection, Mark Bradford at the Venice Biennale, The Center for Urban Pedagogy, Walker Art Center, Cooper Hewitt Design Museum, ICA at Virginia Commonwealth University, and The New Museum. Munro also serves as Associate Professor of Communication Arts at the Otis College of Art and Design in Los Angeles, and Advisor, Founding Faculty, and Chair Emeritus at Vermont College of Fine Arts. In the past year he has emerged as one of the most exciting practitioners of community-engaged design and as an influential scholar known for his contributions to W. E. B. Du Bois’s Data Portraits: Visualizing Black Americapublished by Princeton Architectural Press in late 2018. In workshops and lectures he addresses post-colonial relationships between design and marginalized communities and offers practical ways for educators and practitioners to decolonize the way design is taught and to create inclusive new frameworks. His design and writing has been published in books, exhibitions, and websites in Germany, Japan, Korea, the US, and the UK including Chronicle Books, IDEA magazine, Eye, and Slanted magazine. Munro earned a BFA from Rhode Island School of Design and an MFA from California Institute of the Arts.

    Ramon Tejada is a (New Yorkino / American) designer ( as Estudio Ramon ) and educator based in Providence, RI. He works in a hybrid design/teaching practice focusing on collaboration, inclusion, unearthing and the responsible expansion of design – a practice he has named “puncturing.” Ramon is an Assistant Professor in the Graphic Design Department at RISD.

    Sarah Williamsis currently an Associate Professor of Technology and Urban Planning at the Massachusetts Institute of Technology (MIT) where she also directs the Civic Data Design Lab and chairs MIT’s new undergraduate program in Urban Science. Williams combines her training in computation and design to create communication strategies that expose urban policy issues to broad audiences and create civic change. She calls the process Data Action, which is also the name of her recent book published by MIT Press. Williams is co-founder and developer of Envelope.city, a web-based software product that visualizes and allows users to modify zoning in New York City. Before coming to MIT, Williams was Co-Director of the Spatial Information Design Lab at Columbia University’s Graduate School of Architecture Planning and Preservation (GSAPP), where she was a member of the Million Dollar Blocks team which is well known for using visualization to highlight the costs of incarceration. Her design work has been widely exhibited including work in the Guggenheim, the Museum of Modern Art (MoMA), Venice Biennale, and the Cooper Hewitt Museum. Williams has won numerous awards including being named top 25 planners in the technology and Game Changer by Metropolis Magazine. Check out her upcoming exhibition, Visualizing NYC 2021, at the Center for Architecture in New York City which opens November 17th.

  • Juned Siddique, DrPH

    Associate Professor

    Departments of Preventive Medicine and Psychiatry and Behavioral Sciences

    Northwestern University Feinberg School of Medicine

    Bio:  Dr. Siddique’s research efforts focus on developing statistical methods for handling incomplete or missing data. He applies these methods to a range of problems including rater bias, participant dropout, data harmonization in individual participant data analysis, and measurement error. He collaborates closely with lifestyle intervention researchers and is interested in the analysis of diet and physical activity data.

    Title:  “Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study”

    Abstract: In lifestyle intervention trials, where the goal is to change a participant’s weight or modify their eating behavior, self-reported diet is a longitudinal outcome variable that is subject to measurement error. We propose a statistical framework for correcting for measurement error in longitudinal self-reported dietary data by combining intervention data with auxiliary data from an external biomarker validation study where both self-reported and recovery biomarkers of dietary intake are available. In this setting, dietary intake measured without error in the intervention trial is missing data and multiple imputation is used to fill in the missing measurements. Since most validation studies are cross-sectional, they do not contain information on whether the nature of the measurement error changes over time or differs between treatment and control groups. We use sensitivity analyses to address the influence of these unverifiable assumptions involving the measurement error process and how they affect inferences regarding the effect of treatment. We apply our methods to self-reported sodium intake from the PREMIER study, a multi-component lifestyle intervention trial.

    For more information about the Statistics Seminar Series go here.

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Please note the following talk is only available to faculty members.

     

    ELI UPFAL

    Professor of Computer Science, Brown University

     

    BIG DATA: Where Practice Meets Theory

    Responsible and relevant data science requires rigorous analytical tools or evaluating and improving prediction and presentation accuracy. Here we’ll discuss techniques for enhancing the accuracy of weakly supervised learning and tools for measuring and reducing structural bias in hyperlinked data.

     

    (F4F) Faculty for Faculty Research Talks

    DSI Faculty for Faculty Research Talks are an opportunity for faculty to share current data science-related research activities with other faculty colleagues in an informal environment. The talks are presented at a very general level, to stimulate discussion and interdisciplinary interchange of ideas.

    Our goal is to provide a networking venue that promotes research collaborations between faculty across all disciplines; awareness of the breadth of data science-related research at Brown; and a forum for faculty to share their expertise with one another. Participation will be limited to faculty members.

  • Advance-CTR Services Core users share the research projects they developed with help from the cores:

    Register now!

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • PLEASE NOTE THE SPECIAL TIME FOR THIS EVENT!  

    DAMON CENTOLA

    Professor, Annenberg School for Communication, School of Arts and Sciences, and the School of Engineering and Applied Sciences

    Director of the Network Dynamics Group

    Senior Fellow, Leonard Davis Institute of Health Economics

    University of Pennsylvania

     

    HOW SIMILAR MEANING SYSTEMS EMERGE ACROSS DIVERSE CULTURES

    Categories are everywhere. Everything we touch, eat, and do is part of a system of categories that define our world. Although there is widespread agreement in society about the categories used for familiar objects – from furniture to fruit – recent studies have found that individuals vary widely in how they categorize novel and ambiguous phenomena. This individual variation has led influential theories in cognitive and social science to suggest that communication in large social groups introduces path dependence in category formation, which is expected to lead separate populations toward divergent cultural trajectories. Remarkably, however, similar category systems for color, animals, plants, and shapes are found to arise independently across many cultures around the world. How is it possible for diverse populations, consisting of individuals with significant variation in how they categorize the world, to nevertheless independently construct similar category systems? We investigated this puzzle experimentally by creating an online “Grouping Game” in which we observed how people in small and large populations collaboratively constructed category systems for a continuum of ambiguous stimuli. In this talk, I present new findings showing that solitary individuals and small groups produce highly divergent category systems; however across independent trials with unique participants, large populations consistently converge on highly similar category systems. I present a new theory of critical mass dynamics in social networks, which accurately predicts this process of “scale-induced category convergence”. These findings reveal how large communication networks can filter lexical diversity among individuals to produce replicable society-level patterns, yielding unexpected implications for cultural evolution.

    BIOGRAPHY

    Damon Centola is a Professor in the Annenberg School for Communication, the School of Arts and Sciences, and the School of Engineering and Applied Sciences at the University of Pennsylvania, where he is Director of the Network Dynamics Group and Senior Fellow at the Leonard Davis Institute of Health Economics.

    His research addresses social networks and behavior change. His work has been published across several disciplines in journals such as Science, Proceedings of the National Academy of Sciences, American Journal of Sociology, and Journal of Statistical Physics. Damon received the American Sociological Association’s Award for Outstanding Research in Mathematical Sociology in 2006, 2009, and 2011; the Goodman Prize for Outstanding Contribution to Sociological Methodology in 2011; the James Coleman Award for Outstanding Research in Rationality and Society in 2017; and the Harrison White Award for Outstanding Scholarly Book in 2019. He was a developer of the NetLogo agent based modeling environment, and was awarded a U.S. Patent for inventing a method to promote diffusion in online networks. He is a member of the Sci Foo community and Fellow of the Center for Advanced Study in the Behavioral Sciences at Stanford University.

    Damon’s research has been funded by the National Science Foundation, the Robert Wood Johnson Foundation, Facebook, the National Institutes of Health, the James S. McDonnell Foundation, and the Hewlett Foundation. He is a series editor for Princeton University Press, and the author of How Behavior Spreads: The Science of Complex Contagions, and Change: The Power in the Periphery to Make Big Things Happen.

    Before coming to Penn, Damon was an Assistant Professor at MIT and a Robert Wood Johnson Fellow at Harvard. Damon’s speaking and consulting clients include Amazon, Microsoft, Apple, Cigna, the Smithsonian Institution, the American Heart Association, the National Academies, the U.S. Army and the NBA. Popular accounts of Damon’s work have appeared in The New York Times, The Washington Post, The Wall Street Journal, Wired, TIME, The Atlantic, Scientific American and CNN.

     

    Data Wednesdays

    The Data Science Initiative joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics and computer science. Please check our events page for more information on these and other events of interest.

  • Nov
    11
    12:00pm - 1:30pm

    Introduction to REDCap

    Zoom

    Workshop Description
    Geared toward new or novice REDCap users, “Introduction to REDCap” answers “what” REDCap is, “why” you want to use it, and goes through the entire life cycle of a REDCap project – from initial setup to data entry and finally exporting your data.

    About the Instructor
    Sarah B. Andrea, PhD, MPH, is a research scientist with the Lifespan Biostatistics Core at Rhode Island Hospital. In addition to providing design and statistical oversight and mentorship, she also conducts research investigating strategies to mitigate race-, class-, and gender-based inequities in health throughout the life course. Dr. Andrea has been working with REDCap for five years.

    Register now!

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Event Flyer 

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering
  • Thomas Jaki, PhD

    Professor of Statistics, Department of Mathematics and Statistics

    Lancaster University and     

    Programme Leader at the MRC Biostatistics Unit, University of Cambridge

     

    Bio: Thomas Jaki is a Professor in Statistics in the Department of Mathematics and Statistics at Lancaster University. His research interests include design and analysis of clinical trials, early phase drug development, personalized medicine and biostatistics.

    An Information-Theoretic Approach for Selecting Arms in Clinical Trials

     Abstract: The question of selecting the “best” amongst different choices is a common problem in statistics. In drug development, our motivating setting, the question becomes, for example: which treatment gives the best response rate or which dose of a treatment gives an acceptable risk of toxicity. In this talk I will introduce a flexible adaptive experimental design that is based on the theory of context-dependent information measures. I will show that the design leads to a reliable selection of the correct arm in the settings of Phase I and Phase II clinical trials.

    For more information about the Statistics Seminar Series go here.

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Biomedical Informaticsis the “interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, driven by efforts to improve human health.” (Kulikowski et al., 2012) Implementation Scienceis “the scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine practice, and, hence, to improve the quality and effectiveness of health services.” (Bauer et al., 2015) Learning Health Systems are systems in which “science, informatics, incentives, and culture are aligned for continuous improvement and innovation, with best practices seamlessly embedded in the delivery process and new knowledge captured as an integral by-product of the delivery experience.” (IOM, 2013)

    The Informatics and Implementation Science Learning Series (I 2 S 2 ) will kick off with introductions to informatics and implementation science, which will be followed by a discussion on bridging these fields to support a learning health system for COVID-19 in Rhode Island.

    Registration Via Zoom Requiredhttps://brown.zoom.us/meeting/register/tJUqcOygrzkiH9aP99w9tw79MB9JKiB73vn0

    I 2 S 2 covers the breadth of topics in effectively using data and technology to advance biomedical discovery and healthcare delivery. Each learning activity (seminar, journal club, workshop, or tutorial) features methods, applications, or resources that are aligned with components of a learning health system. This series is a joint initiative between the Brown Center for Biomedical Informatics , Brown Department of Psychiatry and Human Behavior Implementation Science Core , Rhode Island Quality Institute , and Advance Clinical and Translational Research (Advance-CTR) .

    Biology, Medicine, Public Health, Teaching & Learning, Training, Professional Development
  • PLEASE NOTE THE SPECIAL DAY AND TIME OF THIS TALK!

     

    MARC TIMME

    Center for Advancing Electronics Dresden (CFAED), Technical University Dresden

    Human Activity Data for Understanding Collective Mobility Dynamics

    We share an increasing amount of data about our activities, including about where we are and where we move at which times when we work, sleep and eat, and with whom we communicate. While the entirety of the personal data raises a number of questions, predict, and potentially influence and control several essential collective dynamical data features of systems fundamental in daily life. In this talk, Timme will provide examples about how mobility and activity data may improve our understanding and design of ride-hailing and ride-sharing mobility systems (1, 2, 3) and at the same time support the prediction and mitigation strategies against natural phenomena such as the COVID-19 pandemics (4, 5).

    1) Anomalous supply shortages from dynamic pricing in on-demand mobility

    2) Topological universality of on-demand ride-sharing efficiency

    3) Either fair or efficient – Hysterisis-induced inefficiencies in on-demand ride-hailing, in prep.

    4) COVID-19 in (South) Africa

    5) Agent-based activity and mobility simulations

     

    Biography

    Marc Timme, Prof. Dr. rer. nat., MA became TU Dresden Strategic Professor and Chair for Network Dynamics in 2017, bridging the cfaed with the Institute for Theoretical Physics. Marc is interested in building mathematical, conceptual, and algorithmic foundations towards an understanding of the collective nonlinear dynamics of networks, applications fields include biological and bio-inspired technical systems, future mobility, network economy & sustainability as well as network inverse problems of inference, design, and control. Enjoys swimming, hiking, philosophy – and science.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences
  • THOMAS TRIKALINKOS

    Professor of Health Services, Policy, and Practice; Director of the Center for Evidence Synthesis in Health at Brown University

    MODELING THE SARS-CoV-2 EPIDEMIC IN RHODE ISLAND

    Trikalinos will discuss local modeling of the SARS-CoV-2 epidemic with the distinct goals of inferring the current status of the epidemic, predicting a consequential resurgence, and optimizing health resource allocations. He will describe an ensemble of Bayesian epidemiological models to fit the time series of data testing, seroprevalence, surveys, hospitalizations (non-ICU and ICU admissions, census, discharges), and deaths (in and out of hospital). His research is in methods and applications of evidence synthesis and decision making under risk and ambiguity. 

    BIOGRAPHY

    Tom Trikalinos studied medicine in Greece. Currently, he directs the Center for Evidence Synthesis in Health at Brown University (CESH). Faculty and staff work on novel methodologies for comparative effectiveness research, with emphasis on the steps of evidence synthesis (through systematic review and meta-analysis), and evidence contextualization (through decision-making and economic analysis). Trikalinos and his colleagues strive to modernize and optimize the processes of evidence-synthesis by porting methodologies from computer science and applied mathematics. His current research is on decision-making under deep uncertainty.

     

    Decoding Pandemic Data: A Series of Interactive Seminars

    This seminar series brings to Brown experts who are directly engaged in COVID-related data-driven research activities. Instead of the usual long-format lecture, each seminar will feature a 20 to 30–minute presentation by the speaker, followed by a moderated question and answer period. All seminars are virtual and open to the public.

  • Alekandra Slavković, PhD

    Professor, Departments of Statistics and Public Health Sciences

    Associate Dean for Graduate Education, Eberly College of Science

    Title: Valid statistical inference with privacy constraints

    Abstract: Limiting the disclosure risk of sensitive data and statistical analyses is a long-standing problem in statistics. Differential privacy (DP), provides a framework for a strong provable privacy protection against arbitrary adversaries while allowing the release of summary statistics and potentially synthetic data. DP methods/mechanisms require the introduction of randomness which reduces the utility of the results especially in finite samples. In this talk we give an overview of statistical data privacy and its links to DP. We also describe a general framework, built on sound statistical principles from measurement error, robustness and the likelihood-based inference, and give specific examples of how to achieve optimal statistical inference under formal privacy, focused on survey and census data.

    Bio:  Aleksandra Slavković is Professor of Statistics and Public Health Sciences at Pennsylvania State University. Her research interests include statistical disclosure limitation, algebraic statistics, characterization of discrete distributions, and application of statistics to social sciences.

    For more information about the Statistics Seminar Series go here.

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  •  
    PARKER VANVALKENBURGH

    Stanley J. Bernstein Assistant Professor of Social Sciences, Director of Brown Digital Archaeology Laboratory

    Interregional Archaeology and Big Data: Studying Ancient Landscapes at Scale Using the Geospatial Platform for Andean, Culture History and Archaeology (GeoPACHA)
    This is an opportunity for faculty to share current data science–related research activities with other faculty colleagues in an informal and interdisciplinary environment. More about this series on our website.
    Please register for this talk here. Faculty only, please.
  • KEVIN KNUDSON

    University of Florida

     

    APPROXIMATE TRIANGULATIONS OF GRASSMANN MANIFOLDS

    The Grassmann manifolds G(k,n) of k-planes in n-dimensional Euclidean space are important spaces in a variety of topological contexts. However, if one asks for a specific triangulation of one of these spaces the result is usually disappointment. In this talk I will discuss a method to build what I call an approximate triangulation via persistent homology techniques. Several examples will be included.

     

    BIOGRAPHY

    Kevin Knudson is a professor in the Department of Mathematics at the University of Florida, where he currently serves as department chair. His primary mathematical interests lie in the area of applied topology with a special emphasis on multiparameter persistent homology and discrete Morse theory. Knudson is the co-host, with freelance writer Evelyn Lamb, of the My Favorite Theorem podcast. When not thinking about math or administrative tasks, he enjoys kayaking, playing the guitar (poorly), and cooking.

     

    Data Wednesdays

    The Data Science Initiative joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics and computer science. Please check our events page for more information on these and other events of interest.

  • BHRAMAR MUKHERJEE

    University of Michigan School of Public Health

    Predictions, Role of Interventions and the Crisis of Virus in India: A Data Science Call to Arms
    India, the world’s largest democracy with 1.34 billion people, has undergone five phases of lockdown from March 25-June 30. However, the virus curve has not turned the corner yet and the peak seems to be on the distant horizon. In this group presentation, we will discuss an extended SIR model for predicting case-counts in India. We will evaluate the national lockdown as a non-pharmaceutical intervention through various public health relevant metrics and illustrate that regional variation makes the concept of a national peak nebulous. We finally end with describing recent methodological innovations regarding incorporating selective and imperfect viral testing in an extended SEIR model for COVID-19.
     
    Biography

    Bhramar Mukherjee is John D. Kalbfleisch Collegiate Professor and Chair, Department of Biostatistics; Professor, Department of Epidemiology, University of Michigan (UM) School of Public Health; Research Professor and Core Faculty Member, Michigan Institute of Data Science (MIDAS), University of Michigan. She also serves as the Associate Director for Quantitative Data Sciences, The University of Michigan Rogel Cancer Center. She is the cohort development core co-director in the University of Michigan’s institution-wide Precision Health Initiative. Her research interests include statistical methods for analysis of electronic health records, studies of gene-environment interaction, Bayesian methods, shrinkage estimation, analysis of multiple pollutants. Collaborative areas are mainly in cancer, cardiovascular diseases, reproductive health, exposure science and environmental epidemiology. She has co-authored more than 250 publications in statistics, biostatistics, medicine and public health and is serving as PI on NSF and NIH funded methodology grants. She is the founding director of the University of Michigan’s summer institute on Big Data. Bhramar is a fellow of the American Statistical Association and the American Association for the Advancement of Science. She is the recipient of many awards for her scholarship, service and teaching at the University of Michigan and beyond. Including the Gertrude Cox Award, from the Washington Statistical Society in 2016 and most recently the L. Adrienne Cupples Award, from Boston University in 2020. She and her team have been modeling the pandemic in India for the last eight months.

    Decoding Pandemic Data: A Series of Interactive Seminars

    This seminar series brings to Brown experts who are directly engaged in COVID-related data-driven research activities. Instead of the usual long-format lecture, each seminar will feature a 20 to 30–minute presentation by the speaker, followed by a moderated question and answer period. All seminars are virtual and open to the public.

  • Jared Murray, PhD

    Assistant Professor

    Information, Risk and Operations Management

    University of Texas at Austin, McCombs School of Business

     

    Title: “Scaling Bayesian Probabilistic Record Linkage with Post-Hoc Blocking: An Application to the California Great Registers”

    Abstract:  Probabilistic record linkage (PRL) is the process of determining which records in two databases correspond to the same underlying entity in the absence of a unique identifier. Bayesian solutions to this problem provide a powerful mechanism for propagating uncertainty due to uncertain links between records (via the posterior distribution). However, computational considerations severely limit the practical applicability of existing Bayesian approaches. We propose a new computational approach, providing both a fast algorithm for deriving point estimates of the linkage structure that properly account for one-to-one matching and a restricted MCMC algorithm that samples from an approximate posterior distribution. Our advances make it possible to perform Bayesian PRL for larger problems, and to assess the sensitivity of results to varying prior specifications. We demonstrate the methods on a subset of an OCR’d dataset, the California Great Registers, a collection of 57 million voter registrations from 1900 to 1968 that comprise the only panel data set of party registration collected before the advent of scientific surveys.

    Bio:  Dr. Murray is an assistant professor of statistics in the Department of Information, Risk, and Operations Management at the McCombs School of Business, University of Texas in Austin. Until July of 2017 he was a visiting assistant professor in the Department of Statistics at Carnegie Mellon University. He completed his Ph.D. in Statistical Science at Duke University with Jerry Reiter. He also holds a B.S. in Interdisciplinary Mathematics (Statistics) from the University of New Hampshire and an M.S. in Statistical Science from Duke University. His current research interests are in developing flexible Bayesian models for heterogeneous and structured data, with applications to causal inference, record linkage, multiple imputation for missing data, and latent variable modeling.

    For more information about the Statistics Seminar Series go here.

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • JESSICA HEKMAN

    Computational Biologist, Broad Institute

    Canine Behavioral Genomics: tackling complex traits with citizen science

    Dogs have proven to be excellent models for diseases such as cancer. What about behavioral issues such as anxiety and aggression? Karlsson Lab at the Broad Institute of MIT and Harvard is taking a citizen science approach to identification of genetic variants underlying behavioral differences in dogs. Pet owners answer behavioral surveys about their dogs on a website, darwinsark.org, and submit saliva swabs for sequencing. Samples are sequenced using low-pass coverage followed by imputation to generate about 10 million variants, which are used with behavioral survey data in GWAS to identify associations between variants and traits. Current work in Karlsson Lab focuses on adapting existing tools to the new Canis familiaris reference sequence; developing a targeted sequencing panel to increase sequencing coverage over areas of the genome of particular interest; and building an imputation panel for structural variants, to allow future GWAS to include variants such as SINEs or copy number variants in addition to SNPs.

     

    Biography

    Jessica Hekman worked as a computer programmer for 12 years until deciding to go back to school to study canine behavior. She received her DVM/MS degrees at Tufts Cummings School of Veterinary Medicine in 2012. Her MS research, in Comparative Biomedical Sciences, used behavioral observation and salivary cortisol in identification and description of stress behaviors in healthy hospitalized dogs. She completed a shelter medicine specialty veterinary internship at the Maddie’s Shelter Medicine Program at the University of Florida in 2013. She completed her PhD at the University of Illinois at Urbana-Champaign in 2017, where she studied transcriptional differences in regions of the brain involved in the hormonal stress response between tame and aggressive foxes. She worked as a postdoctoral associate in Karlsson Laboratory at the Broad Institute of MIT and Harvard, studying canine behavioral genomics using a citizen science approach, and is now a Computational Biologist on the same team.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences
  • samantha nazareth
    Oct
    21

    Please join us on Wednesday, October 21st at 1PM EST for a presentation by Dr. Samantha Nazareth: “Web 3.0 and the Future of Medicine.” In this presentation, Dr. Nazareth will review the impact of the Internet of Healthcare Things (IoHT), artificial intelligence and machine learning, blockchain, and other technologies on the future of how we practice medicine.

    Dr. Samantha Nazareth (College ’04, Medicine ’08) is a gastroenterologist based in NYC who serves as Chief Medical Officer of a blockchain company and advises early-stage start-ups, venture capitalists, and corporations on clinical validation and use case perspectives for digital health solutions.

    *Location: Zoom meeting

    *Time: Wed. October 21st @ 1:00-2:00 EST

    ***RSVP using the Eventbrite link, and you will receive a confirmation email with the Zoom link***

    Contact us at [email protected] or [email protected] with any questions. We hope to see you there!

    Biology, Medicine, Public Health, Entrepreneurship, Mathematics, Technology, Engineering
  •  

    EVAN RAY

    Research Assistant Professor, Department of Biostatistics and Epidemiology, Reich Lab, University of Massachusetts, Amherst

     

    THE COVID-19 FORECAST HUB: USING STATISTICS AND DATA SCIENCE TO SUPPORT DECISION MAKING IN A PANDEMIC

    With a variety of local, state, and even national COVID-19 related policies being implemented across the country and world, there is an opportunity to learn about the relative effectiveness of those policies, to guide policymaking during the current pandemic and beyond. However, accurately estimating policy effects is challenging. Beyond the issues that are faced for any policy, such as lack of randomization and difficulties in disentangling underlying time trends from a policy effect, evaluation of COVID-19 policies is complicated by additional challenges related to infectious disease dynamics and lags, lack of direct observation of key outcomes, and a multiplicity of interventions occurring on an accelerated time scale. This session will provide a high-level overview of the key designs and considerations when estimating policy effects, with the goal of helping attendees understand how to weigh the strengths and limitations of particular studies aiming to estimate the effects of policy interventions.

    Decoding Pandemic Data: A Series of Interactive Seminars

    This seminar series brings to Brown experts who are directly engaged in COVID-related data-driven research activities. Instead of the usual long-format lecture, each seminar will feature a 20 to 30–minute presentation by the speaker, followed by a moderated question and answer period. All seminars are virtual and open to the public.

     
  • Mauricio Sadinle, PhD

    Assistant Professor, Department of Biostatistics

    University of Washington, School of Public Health

    Sequentially additive nonignorable missing data modelling using auxiliary marginal information

    Abstract: We study a class of missingness mechanisms, referred to as sequentially additive nonignorable, for modelling multivariate data with item nonresponse. These mechanisms explicitly allow the probability of nonresponse for each variable to depend on the value of that variable, thereby representing nonignorable missingness mechanisms. These missing data models are identified by making use of auxiliary information on marginal distributions, such as marginal probabilities for multivariate categorical variables or moments for numeric variables. We prove identification results and illustrate the use of these mechanisms in an application.

    Article: https://doi.org/10.1093/biomet/asz054

    Bio: Mauricio Sadinle, PhD is an Assistant Professor in the Department of Biostatistics at the University of Washington. Previously, he was a Postdoctoral Associate in the Department of Statistical Science at Duke University and the National Institute of Statistical Sciences, working under the mentoring of Jerry Reiter. He completed his PhD in the Department of Statistics at Carnegie Mellon University, where his advisor was Stephen E. Fienberg. Dr. Sadinle’s undergraduate studies are from the National University of Colombia in Bogota, where he majored in statistics.  Dr. Sadinle’s methodological research mainly focuses on 1. Record linkage techniques to combine datafiles that contain information on overlapping sets of individuals but lack unique identifiers and 2. Nonignorable missing data modeling, and the usage of auxiliary information to identify nonignorable missing data mechanisms. Dr. Sadinle also has experience working with social network models for valued ties, capture-recapture models in the context of human rights violations, and set-valued classifiers that output sets of plausible labels for ambiguous sample points.

    For more information about the Statistics Seminar Series go here.

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  •  

    STEVEN STROGATZ

    Jacob Gould Schurman Professor of Applied Mathematics, Cornell University

    Networks of oscillators that synchronize themselves

    Populations of coupled oscillators are pervasive in the natural world, from swarms of rhythmically flashing fireflies to groups of pacemaker cells in the heart. Some systems of oscillators have the amazing ability to synchronize themselves, such that all the oscillators end up firing in unison, no matter how disorganized they were at the start. In this (hopefully) entertaining Zoom talk, Prof. Strogatz will discuss the simplest mathematical model of a self-synchronizing system, the so-called Kuramoto model, and discuss how it behaves on different kinds of networks. Using techniques from nonlinear dynamics, numerical linear algebra, and computational algebraic geometry, we will discuss new bounds, conjectures, and open problems about the densest networks that do *not* always synchronize and the sparsest ones that do. This is joint work with Alex Townsend and Mike Stillman.

    Biography

    Steven Strogatz is an applied mathematician who works in the areas of nonlinear dynamics and complex systems, often on topics inspired by the curiosities of everyday life. He loves finding math in places where you’d least expect it—and then using it to illuminate life’s mysteries, big and small. For example: Why is it so hard to fall asleep a few hours before your regular bedtime? When you start chatting with a stranger on a plane, why is it so common to find that you have a mutual acquaintance? What can twisting a rubber band teach us about our DNA? An award-winning researcher, teacher, and communicator, Strogatz enjoys sharing the beauty of math through his books, essays, public lectures, and radio and television appearances.

    Data Wednesdays

    The Data Science Initiative joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science.

    Mathematics, Technology, Engineering, Physical & Earth Sciences
  • Oct
    14
    12:00pm - 1:30pm

    Introduction to REDCap

    Zoom

    Workshop Description
    Geared toward new or novice REDCap users, “Introduction to REDCap” answers “what” REDCap is, “why” you want to use it, and goes through the entire life cycle of a REDCap project – from initial setup to data entry and finally exporting your data.

    About the Instructor
    Sarah B. Andrea, PhD, MPH, is a research scientist with the Lifespan Biostatistics Core at Rhode Island Hospital. In addition to providing design and statistical oversight and mentorship, she also conducts research investigating strategies to mitigate race-, class-, and gender-based inequities in health throughout the life course. Dr. Andrea has been working with REDCap for five years.

     

    Register now!

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Join the Carney Institute for Brain Science for a conversation focused on neurotechnology featuring:

    • Leigh Hochberg, professor of engineering at Brown University, director of the Center for Neurotechnology and Neurorecovery at Massachusetts General Hospital, and director of the VA RR&D Center for Neurorestoration and Neurotechnology
    • David Borton, assistant professor of engineering at Brown and research biomedical engineer with the VA RR&D Center for Neurorestoration and Neurotechnology

    This event will be moderated by Diane Lipscombe, Reliance Dhirubhai Ambani Director of the Carney Institute, and Christopher Moore, associate director of the Carney Institute.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research
  • Oct
    8
    2:00pm - 3:00pm

    DeepLabCut+ Users Group Meeting

    Zoom


    The Brown DeepLabCut+ Users Group, hosted by the Carney’s Center for Computational Brain Science, will hold its inaugural meeting Thursday, October 8, 2-3 p.m.

    The goal of the user group is to provide a local community for peer support and information sharing, and to guide future decisions on local resources, such as support for running DLC on Oscar.

    In this first meeting, moderated by Jason Ritt, Carney’s scientific director of quantitative neuroscience, and Maria Daigle, research assistant in the Department of Neuroscience, we will discuss the group’s organization, and invite all attendees to share any issues they are facing using DLC in their research, and/or troubleshooting advice. Going forward we expect to collect and share technical documentation, and provide a forum to match users needing help with local expertise.

    Please respond prior to the meeting to this short questionnaire.

    Biology, Medicine, Public Health, CCBS, CTN, Psychology & Cognitive Sciences, Research
  • Advance-K Scholar Sebhat Erquo shares his research and how the program helped him secure a VA Career Development Award:

    • Sebhat Erqou, MD, PhD: “Improving Cardiovascular Care for Veterans Living with HIV”
    • Laura Stroud, PhD: “Leveraging Advance-CTR Resources for the Stress, Trauma, and Resilience (STAR) T32 and COBRE Submissions”

    Register now!

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Data Wednesdays

    The Data Science Initiative joins the Center for Computational and Molecular Biology (CCMB) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science.

     

    JOAN BRUNA

    Assistant Professor of Computer Science, Data Science, and Mathematics, Courant Institute and Center for Data Science, New York University

     

    MATHEMATICAL ASPECTS OF NEURAL NETWORK LEARNING THROUGH MEASURE DYNAMICS

    High-dimensional learning remains an outstanding phenomena where experimental evidence outpaces our current mathematical understanding, mostly due to the recent empirical successes of Deep Learning algorithms. Neural Networks provide a rich yet intricate class of functions with statistical abilities to break the curse of dimensionality, and where physical priors can be tightly integrated into the architecture to improve sample efficiency. Despite these advantages, an outstanding theoretical challenge in these models is computational, ie providing an analysis that explains successful optimization and generalization in the face of existing worst-case computational hardness results. In this talk, I will focus on the framework that lifts parameter optimization to an appropriate measure space. I will cover existing results that guarantee global convergence of the resulting Wasserstein gradient flows, as well as recent results that study typical fluctuations of the dynamics around their mean field evolution. We will also discuss extensions of this framework beyond vanilla supervised learning, to account for symmetries in the function, as well as for competitive optimization.

     

    Biography

    Joan Bruna is an Assistant Professor at Courant Institute, New York University (NYU), in the Department of Computer Science, Department of Mathematics (affiliated), and the Center for Data Science, since Fall 2016. He belongs to the CILVR group and to the Math and Data groups. From 2015 to 2016, he was an Assistant Professor of Statistics at UC Berkeley and part of BAIR (Berkeley AI Research). Before that, he worked at FAIR (Facebook AI Research) in New York. Prior to that, he was a postdoctoral researcher at Courant Institute, NYU. He completed his Ph.D. in 2013 at Ecole Polytechnique, France. Before his Ph.D. he was a Research Engineer at a semi-conductor company, developing real-time video processing algorithms. Even before that, he did a MsC at Ecole Normale Superieure de Cachan in Applied Mathematics (MVA) and a BA and MS at UPC (Universitat Politecnica de Catalunya, Barcelona) in both Mathematics and Telecommunication Engineering. For his research contributions, he has been awarded a Sloan Research Fellowship (2018), an NSF CAREER Award (2019), and a best paper award at ICMLA (2018).

  • Decoding Pandemic Data: A Series of Interactive Seminars

    This seminar series brings to Brown experts who are directly engaged in COVID-related data-driven research activities. Instead of the usual long-format lecture, each seminar will feature a 20 to 30–minute presentation by the speaker, followed by a moderated question and answer period. All seminars are virtual and open to the public.

    ELIZABETH STUART

    Professor of Mental Health, Health Policy and Management, and Biostatistics and Associate Dean for Education, Johns Hopkins University

     

    THE NEED FOR, AND CHALLENGES OF, POLICY EVALUATION DURING THE COVID-19 PANDEMIC

    With a variety of local, state, and even national COVID-19 related policies being implemented across the country and world, there is an opportunity to learn about the relative effectiveness of those policies, to guide policymaking during the current pandemic and beyond. However, accurately estimating policy effects is challenging. Beyond the issues that are faced for any policy, such as lack of randomization and difficulties in disentangling underlying time trends from a policy effect, evaluation of COVID-19 policies is complicated by additional challenges related to infectious disease dynamics and lags, lack of direct observation of key outcomes, and a multiplicity of interventions occurring on an accelerated time scale. This session will provide a high-level overview of the key designs and considerations when estimating policy effects, with the goal of helping attendees understand how to weigh the strengths and limitations of particular studies aiming to estimate the effects of policy interventions.

  • Stephanie Shipp, PhD

    Deputy Director and Professor

    Social and Decision Analytics Division, Biocomplexity Institute

    University of Virginia

    Abstract Title: ”Ethical Principles and Data Science - Repurposing Administrative & Opportunity Data”

    The data revolution is changing the conduct of research as increasing amounts of internet-based and administrative data become accessible for use. At the same time, the new data landscape has created significant tension around data privacy and confidentiality. To bridge this gap, conversations about ethics, privacy, transparency, and reproducibility need to play a prominent role in both research partnerships and policymaking. At the research level, these conversations must be translated to action. We have created a comprehensive framework that forms the foundation to data science problem solving through defining rigorous, flexible, and iterative processes where learning at each stage informs the other stages. Embedded in this framework is close attention to ethics. The Institutional Review Board structure is well known in parts of academia and industry, but our public and local government partners are not always aware of these processes. The IRB framework could help them think about informed consent and privacy, as well as ethical considerations around the benefits and risks to individuals and communities under study. Through case studies, these principles are demonstrated.

    Keywords: confidentiality, ethics, trust but verify, data science framework

    Brief Bio: Data scientists have the opportunity to use their skills to influence and improve society, especially vulnerable populations who need champions. Stephanie Shipp enthusiastically works with communities, policy makers and other data scientists who have also taken that challenge to heart.

    For more information about the Statistics Seminar Series go here.

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Despina Kontos, PhD

    Associate Professor of Radiology

    Department of Radiology

    Perelman School of Public Health

    University of Pennsylvania

    Bio:  Dr. Despina Kontos, Ph.D., is an Associate Professor of Radiology and director of the Computational Biomarker Imaging Group (CBIG) in the Center for Biomedical Image Computing and Analytics (CBICA) at the Radiology Department of the University of Pennsylvania. Dr. Kontos received her C.Eng. Diploma in Computer Engineering and Informatics from the University of Patras in Greece and her MSc and Ph.D. degrees in Computer Science from Temple University in Philadelphia. She completed her postdoctoral training in radiologic physics and biostatistics at the University of Pennsylvania. Her research interests focus on investigating the role of quantitative imaging as a predictive biomarker for guiding personalized clinical decisions in cancer screening, prognosis, and treatment. She is leading several research studies, funded both by the NIH/NCI and private foundations, to incorporate novel quantitative multi-modality imaging measures of breast tumor and tissue composition into cancer risk prediction models.

    Title:  “Radiomic Biomarkers for Deciphering Tumor Heterogeneity”

    Abstract - Breast cancer is a heterogeneous disease, with known inter-tumor and intra-tumor heterogeneity. Established histopathologic prognostic biomarkers generally acquired from a tumor biopsy may be limited by sampling variation. Radiomics is an emerging field with the potential to leverage the whole tumor via non-invasive sampling afforded by medical imaging to extract high throughput, quantitative features for personalized tumor characterization. Identifying imaging phenotypes via radiomics analysis and understanding their relationship with prognostic markers and patient outcomes can allow for a non-invasive assessment of tumor heterogeneity. In this study, we identified and independently validated intrinsic radiomic phenotypes of tumor heterogeneity for invasive breast cancer that have independent prognostic value when predicting 10-year recurrence. The independent and additional prognostic value of imaging heterogeneity phenotypes suggests that radiomic phenotypes can provide a non-invasive characterization of tumor heterogeneity to augment personalized prognosis and treatment.

    For more information about the Statistics Seminar Series, click here.

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Jianqing Fan, PhD, Professor of Statistics

    Frederick L. Moore ’18 Professor of Finance

    Princeton University

    Title: Communication—Efficient Accurate Statistical Estimation

    Abstract: When the data are stored in a distributed manner, direct application of traditional statistical inference procedures is often prohibitive due to communication cost and privacy concerns. This paper develops and investigates two Communication-Efficient Accurate Statistical Estimators (CEASE), implemented through iterative algorithms for distributed optimization. In each iteration, node machines carry out computation in parallel and communicates with the central processor, which then broadcasts aggregated gradient vector to node machines for new updates. The algorithms adapt to the similarity among loss functions on node machines, and converge rapidly when each node machine has large enough sample size. Moreover, they do not require good initialization and enjoy linear converge guarantees under general conditions. The contraction rate of optimization errors is derived explicitly, with dependence on the local sample size unveiled. In addition, the improved statistical accuracy per iteration is derived. By regarding the proposed method as a multi-step statistical estimator, we show that statistical efficiency can be achieved infinite steps in typical statistical applications. In addition, we give the conditions under which one-step CEASE estimator is statistically efficient. Extensive numerical experiments on both synthetic and real data validate the theoretical results and demonstrate the superior performance of our algorithms.

    (Joint work with Yongyi Guo and Kaizheng Wang)

     

    For more information about the Statistics Seminar Series, click here

    Biology, Medicine, Public Health, BioStatsSeminar, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • This month’s Translational Research Seminar features talks from two of our Pilot Project Awardee teams:

    • Valentin Antoci, MD, PhD: “Smart Implant Modifications Can Drive Biology and Function”
    • Adam Olszewski, MD, “Molecular Tools for Diagnosis and Risk Prediction of Central Nervous System Involvement in Non-Hodgkin Lymphoma”

    Register now!

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Data Visualization of Social Networks
    Sep
    10

    The undergraduate Certificate in Data Fluency is for students who wish to gain fluency and facility with the tools of data analysis and its conceptual framework. The program is designed to provide fundamental conceptual knowledge and technical skills, to students with a range of intellectual backgrounds and concentrations, while emphasizing a critical liberal learning perspective. Find out more about the certificate in this information and Q & A session, hosted by professor Linda Clark of DSI and the Sheridan Center.

    Education, Teaching, Instruction, Social Sciences
  • Sep
    9
    12:00pm - 1:30pm

    Advance-CTR REDCap Workshop

    Zoom

    Workshop Description
    Geared toward new or novice REDCap users, this class answers “what” REDCap is, “why” you want to use it, and goes through the entire life cycle of a REDCap project – from initial setup to data entry and finally exporting your data.

    About the Instructor
    Sarah B. Andrea, PhD, MPH, is a research scientist with the Lifespan Biostatistics Core at Rhode Island Hospital. In addition to providing design and statistical oversight and mentorship, she also conducts research investigating strategies to mitigate race-, class-, and gender-based inequities in health throughout the life course. Dr. Andrea has been working with REDCap for five years.

    Register now! 

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Join the Carney Institute for Brain Science for a conversation about how traditional brain recording techniques (MEG/EEG) are coming together with new computational tools to inform new directions for brain science research. 

    This event will be moderated by Diane Lipscombe, Reliance Dhirubhai Ambani Director of the Carney Institute, and Christopher Moore, associate director of the Carney Institute, and it will feature Stephanie Jones, associate professor of neuroscience at Brown University, and Frederike Petzschner, who will join the Carney Institute this year as a fellow. 

    Biology, Medicine, Public Health, CCBS, Psychology & Cognitive Sciences, Research
  • Aug
    11
    3:00pm - 4:00pm

    Carney Innovation Awards Information Session for Faculty

    Zoom (Participants must log in with their Brown accounts)

    Join the Carney Institute for an information session and Q&A about the 2020 call for applications for the institute’s Innovation Awards in Brain Science. The purpose of these awards is to launch innovative projects that have great potential to advance science and benefit society in ways that have major and lasting impact. The Innovation Awards in Brain Science are open to all Brown faculty members conducting brain science research at Brown University or its affiliated hospitals. Applications are due August 31, 2020.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research
  •  

    Mass Polarization in the US and Elsewhere

     

    Jesse Shapiro, Eastman Professor of Political Economy

    Shapiro will discuss trends in mass political polarization across demographic groups within the US and across different countries and will discuss possible causes of these trends in light of the evidence.

     

    Please note this talk is restricted to Brown Faculty only. An event link will be sent to registered participants before the talk.

    To register, please visit the DSI Eventbrite page.

     

     

  • This series of faculty for faculty talks is an opportunity for faculty to share current data science–related research activities with other faculty colleagues in an informal environment. The talks will be presented at a very general level, to stimulate discussion and interdisciplinary interchange of ideas.

    Our goal is to provide a networking venue that promotes research collaborations between faculty across all disciplines; awareness of the breadth of data science–related research at Brown; and a forum for faculty to share their expertise with one another.

    Participation is limited to Brown faculty members. Please click here to register, and the Zoom link will be sent to you before the event. 

    Jonathan Pober, Department of Physics: Mapping the Universe with Radio Astronomy and Big Data 

    The field of “21 cm cosmology” is one with a simple premise: all the neutral hydrogen gas in the Universe can be traced through its unique radio wave emission (the “21 centimeter line”). Mapping the hydrogen in the Universe in this way offers an unparalleled probe of cosmic evolution, the formation of the first stars and galaxies, and the make-up of the Universe. However, the hydrogen signal is swamped by other radio emission – both human-generated and astrophysical – and, to date, it has not been successfully measured. Extracting the signal from these contaminants will require that the radio telescopes performing the observations are modeled with a precision never before achieved in radio astronomy. In this presentation, I will highlight what a successful 21 cm cosmology experiment / analysis pipeline might look like, with an emphasis on the multiple data science challenges that arise as we attempt to make this measurement.

    Register here.

  • Join the Carney Institute for Brain Science for a conversation on how emotions can foster disease prevention behaviors during the COVID-19 pandemic with Oriel FeldmanHall, assistant professor of cognitive, linguistic and psychological sciences at Brown University.

    This event will be moderated by Diane Lipscombe, Reliance Dhirubhai Ambani Director of the Carney Institute, and Christopher Moore, associate director of the Carney Institute.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research
  • Jun
    29
    All Day

    Brown Unconference

    Zoom

    The first Brown Unconference is a remote gathering which provides an opportunity for researchers across campus to come together and explore advances in computational sciences at the intersection of data science and AI with other sciences including biology, physics, chemistry, engineering, neuroscience and cognitive science.

    Our goal is to foster an accessible and welcoming environment open to members of the Brown community across all disciplines and levels of expertise. We want to celebrate Brown’s uniqueness and help foster collaborations across disciplines. Students and postdocs are especially encouraged to present their research to the broader community. The Unconference will include opportunities to meet researchers across scientific interests, hear from invited speakers, and receive feedback from diverse points of view.

    We encourage submissions at all points of the scientific process.

    Important Dates

    • Abstract Submission Deadline: June 15, 2020
    • Conference: June 29-30, 2020

    Schedule
    We will host the following events distributed across the two days of the conference. Please find instructions on how to get involved in the next section. The detailed schedule will be published closer to the conference.

    • Lightning talks: 2-3 minute talks (2 slides max) – for those who may be in the early stages of their research, to introduce themselves, share their interests, pitch projects, or simply network with other members of the conference.
    • Short talks: 12 minute talks – for those ready to present their research in an informal setup. Presented research can be in progress and data can be preliminary.
    • Networking & Mind Match: Tailored social and networking programming. The unconference is a safe space to share ideas so feel free to send work in progress. Members of the Brown community from all academic backgrounds are encouraged to submit an abstract!

    Register to Attend
    Register to attend the conference virtually. By registering, you will receive a notification when we announce the schedule with links to the Crowdcast pages.

    Submit an Abstract
    If you’re interested in talking about your research, please submit an abstract.

    Biology, Medicine, Public Health, CCBS, Psychology & Cognitive Sciences, Research
  • Inaugural DSI Faculty for Faculty Research Talk  

    This is an opportunity for faculty to share current data science–related research activities with other faculty colleagues in an informal and interdisciplinary environment. More about this series on our website.

    Please register for this event by Friday, June 26, 8 am. And please note, this presentation/discussion is restricted to faculty members. 

    Roberta DeVito, DSI and Biostatistics

    Reproducibility in the Big-Data Era

    Researchers are facing the urgent challenge of efficiently dealing with a large amount of experimental data. These big and high-throughput data are a rich, complex, and diverse collection of high-dimensional data sets and have the potential to lead to discoveries, advances, and knowledge that were never accessible before via compelling statistical analysis. With these different sources of big-data sets, new statistical analyses that integrate multiple, somewhat diverse studies, are crucial to understand and gain knowledge in high-dimensional statistical research.

    In this talk, I will talk about my research on both theoretical and computational methods for dimension reduction allowing for the joint analysis of multiple high-throughput experiments, simultaneously achieving two goals: a) to capture common component(s) across studies and b) to estimate the specificness that is unique to each study. When considering multiple studies, some measurements reappear across studies, and the true signal is more likely to be reproducible among the studies. However, high throughput experiments display both artifactual and intrinsic sources of variation. I will then present several different applications: microarray gene expression in cancer, nutritional epidemiological data in seven different countries, 12 brain regions in tissue studies.

  • Carney SciCom is a two-part workshop for Brown University trainees focused on science communication skills. The goal of these workshops is to empower trainees to share their research with broad and diverse audiences. In this session, participants will learn best practices for designing interactive scientific posters.

    Torrey Truszkowski, research compliance manager in the Office of Research Integrity at Brown, will demonstrate how to design interactive, dynamic scientific posters. Sara Feijo, communications and outreach manager at the Carney Institute for Brain Science, will share tips for presenting posters at conferences.

    Design experience is not required for this workshop.

    Note: This is the second workshop in the series. The first workshop, entitled “Carney SciCom Workshop: Scientific poster design,” will be held on Tuesday, June 16. 

  • COVID-19 DATA TUTORIAL III

    We’ll look at COVID-19 data from the COVID Tracking Project and walk through statistical techniques for using the data to infer how many new infections are being generated by the average infectious person. Minimal prerequisite knowledge will be assumed, and all are welcome.

    Please register for the event below!

     

    Please visit our GitHub page and also Babylon House for more information on these events. 

    Hosted by Samuel Watson, Dir. of Graduate Studies, Data Science Initiative. Organized by the Data Science Initiative

  • Carney SciCom is a two-part workshop for Brown University trainees focused on science communication skills. The goal of these workshops is to empower trainees to share their research with broad and diverse audiences. In this session, participants will learn best practices for designing informative and engaging scientific posters.

    Andrew Creamer, Brown University Library’s scientific data management specialist, and Kelsey Sawyer, biomedical life sciences librarian, will discuss scientific criteria for successful posters, and share resources for designing conference posters. Jaci DaCosta, art director in the Office of University Communications, will discuss design principles — including typography, contrast and accessibility — and demonstrate how to adapt designs for multimedia. DaCosta will develop a poster template, which will be shared with participants following the workshop.

    Design experience is not required for this workshop. However, it is recommended that participants familiarize themselves with Adobe InDesign. Introductory courses to the Adobe Creative Suite are available through Brown’s LinkedIn Learning in Workday.

    RSVP is required. 

    The second workshop in this series, entitled “Carney SciCom Workshop: Interactive scientific posters,” will be held on June 23.

  • Jun
    12
    8:45am - 11:40am

    Rhode Island IDeA Symposium

    Join us for this year’s annual symposium on June 11-12, 2020, which showcases research from Rhode Island’s COBRE, INBRE, ECHO, and CTR IDeA programs. 

    This year, two keynote speakers will present research around the theme of aging:

    • John Sedivy, PhD, “Retrotransposon Activation as a Mechanism of Age-associated Sterile Inflammation: Repurposing HIV Drugs to Treat Chronic Diseases of Aging”
    • Reisa Sperling, MD: “Can We Predict and Treat Alzheimer’s Disease a Decade before Dementia? (and Why We Must!)”

    Nine investigators from RI’s IDeA programs will also deliver science talks. See the full agenda or register on the conference website: sites.brown.edu/ideari.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Jun
    11
    1:00pm - 3:35pm

    Rhode Island IDeA Symposium

    Join us for this year’s annual symposium, on June 11-12, 2020, which showcases research from Rhode Island’s COBRE, INBRE, ECHO, and CTR IDeA programs. 

    This year, two keynote speakers will present research around the theme of aging:

    • John Sedivy, PhD, “Retrotransposon Activation as a Mechanism of Age-associated Sterile Inflammation: Repurposing HIV Drugs to Treat Chronic Diseases of Aging”
    • Reisa Sperling, MD: “Can We Predict and Treat Alzheimer’s Disease a Decade before Dementia? (and Why We Must!)”

    Nine investigators from RI’s IDeA programs will also deliver science talks. See the full agenda or register on the conference website: sites.brown.edu/ideari.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Jun
    5

    Join the Carney Institute for Brain Science for a conversation about the future of robotics and ethical concerns with Stefanie Tellex, associate professor of computer science at Brown University. 

    This event will be moderated by Diane Lipscombe, Reliance Dhirubhai Ambani Director of the Carney Institute, and Christopher Moore, associate director of the Carney Institute.

  • Join us Friday, June 5 at 12 PM for a videoconference discussion of COVID-19 data.

    We will also use a chat system called Babylon House for facilitating the discussion. You can go ahead and join that classroom here:

    https://babylon.house/projects/9282c066-ff77-46d2-8a63-394af6ff5105/join

    If you’d like a very short introduction to using Babylon House as a meeting participant, check out this video: https://youtu.be/RZB2W1R3t3c

    Last time, we explored publicly available data sets and discussed some of the challenges inherent in drawing inferences and making predictions. You can find information from that session (including a Jupyter notebook) here: https://browndsi.github.io/covid19/

    This time we’ll explore methods for estimating the crucially important quantity Rₜ (the average number of new infections generated by each infectious individual, at time t). The discussion will be accessible, introducing the data analysis tools we’ll use along the way.

    Hosted by Samuel Watson, Director of Graduate Studies, Data Science Initiative.

    Please sign up with the ‘Register Here’ link above to indicate that you are participating.

  • Four-week workshop on the Python programming language, web scraping, and data cleaning for increasing data fluency of graduate and post-graduate students.

    Weekdays of June 1-26, from 10am to 3pm

    The Center for Computation and Visualization (CCV) is offering an introductory workshop on the Python programming language, web scraping, and data cleaning, targeted toward graduate students and postdocs in the humanities and social sciences. Python is a versatile computer programming language, useful in many contexts including collecting, cleaning, analyzing, and visualizing data. No experience with coding is required. Faculty and staff members can register if space is available.

    In the first two weeks of the workshop, we will learn Python basics with topics covering:

    • container types such as variables, lists, dictionaries, and arrays,
    • control flow techniques such as if and while statements, for loops, and list comprehensions,
    • functions to manipulate data,
    • simple speed and memory profiling,
    • and visualizations using matplotlib.

    In the third week, we will use web scraping to collect data from the web. Web scraping allows us to automate data collection from websites of varying underlying formats. Attendees will learn topics covering:

    • the basics of web page structures (HTML, CSS),
    • inspecting the page source underlying a web page using developer tools in Google Chrome,
    • the fundamentals of scraping several different web pages of varying complexity,
    • controlling crawl rates and monitoring the scraping loop,
    • and scraping a multi-page web query into a dataframe.

    In the fourth week, attendees will learn to clean and process the scraped data using the pandas library. Topics covered will be:

    • reading in and manipulating the scraped data from csv and excel files, and sql databases,
    • filtering and modifying the scraped data,
    • visualizing the data,
    • and calculating summary statistics.

    The workshop runs June 1-26. Each day will consist of a morning lecture (10am-noon), lunch, and hands-on exercises (1pm-3pm). Participants will have the opportunity to work on their own data-intensive projects with help from CCV data scientists during the third and fourth weeks. The workshop is supported by the Data Science Initiative’s NSF-TRIPODS grant.

    The workshop runs weekdays from June 1 - 26, from 10am to 3pm. Space is limited to 20 people.

    Register here.

  • Now Friday, June 5!

    Join us at 12 PM for a videoconference discussion of COVID-19 data.

    We will also use a chat system called Babylon House for facilitating the discussion. You can go ahead and join that classroom here:

    https://babylon.house/projects/9282c066-ff77-46d2-8a63-394af6ff5105/join

    If you’d like a very short introduction to using Babylon House as a meeting participant, check out this video: https://youtu.be/RZB2W1R3t3c

    Last time, we explored publicly available data sets and discussed some of the challenges inherent in drawing inferences and making predictions. You can find information from that session (including a Jupyter notebook) here: https://browndsi.github.io/covid19/

    This time we’ll explore methods for estimating the crucially important quantity Rₜ (the average number of new infections generated by each infectious individual, at time t). The discussion will be accessible, introducing the data analysis tools we’ll use along the way.

    Hosted by Samuel Watson, Director of Graduate Studies, Data Science Initiative.

     

    Please sign up with the ‘Register Here’ link above to indicate that you are participating.

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Social Sciences, Teaching & Learning
  • Starting on Thursday, May 28 at 12 PM noon Eastern Time, we’ll begin holding live sessions to review content from Data Gymnasia:

    https://mathigon.org/data-gymnasia

    These sessions are intended to help incoming DSI students prepare for the program, but they are open to anyone who is interested in the learning content. They are free of charge.

    For the first session, we will treat the first four sections of the Data Science Pipeline course:

    https://mathigon.org/course/intro-data-pipeline/introduction

    We recommend that you work through those sections prior to class, but you are welcome to participate whether you do or not. If you don’t have experience with Python, or if you want a refresher, you might also complete some of the sections in the Intro to Python course:

    https://mathigon.org/course/programming-in-python/introduction

    The class session will be facilitated using Babylon House:

    https://babylon.house/projects/a5543fce-71d1-4256-89cd-70845ee2cafb/join

    You can see a video on how Babylon House works on the landing page (https://babylon.house), and you can see a shorter student guide at https://youtu.be/RZB2W1R3t3c

    Hosted by Samuel Watson, Director of Graduate Studies, Data Science Initiative.

     

    Please sign up with the ‘Register Here’ link above to indicate that you are participating.

  • May
    27

    Babylon House Webinar

    Babylon House is a messaging web app for engaging all of your students conversationally during classtime. Student messages are intelligently grouped into columns, and instructors or TAs can modify the grouping and respond by group or individually.

    This tool has been used effectively by Brown instructors to keep students actively learning during videoconference classes.

    It’s easy to set up and get started, since it uses Google for authentication. You can run your whole class through it using pre-scripted Lessons, or just use it improvisationally for its conversation features.

    On Wednesday, May 27 at 12 PM noon, we’ll have a live Zoom+BH session to walk through some of the features, answer any questions you might have, and help get you up and running with Babylon House.

    Babylon House link: https://babylon.house/projects/18694f85-6861-427d-af2c-6a2cce646fa4/join https://babylon.house

    Hosted by Samuel Watson, Director of Graduate Studies, Data Science Initiative.

     

    Please sign up with the ‘Register Here’ link above to indicate that you are participating.

  • Carney Methods Meetups

    Join the Carney Institute for a weekly informal gathering on methods for brain science, featuring rotating topics selected by you, the Brown brain science community! Please vote for next week’s topic using this form.

     

    This week’s topic is “Dynamical Models of Neural Systems”, presented by Bjorn Sandstede, Ph.D. Bjorn is a Professor of Applied Mathematics and is the Director of the Data Science Initiative.

     

    Please note, this workshop requires you to be logged into Zoom through your Brown account. Click to learn more.

    Biology, Medicine, Public Health, CCBS, Graduate School, Postgraduate Education, Psychology & Cognitive Sciences, Research
  • Natalie Dean, University of Florida

    In this talk, I provide an overview of the evaluation of candidate COVID-19 vaccines, with a focus on the design of large Phase III efficacy trials. In these trials, thousands of participants are individually randomized to investigational vaccine or placebo control. I discuss our group’s ongoing research into adaptive and flexible trial strategies tailored to the outbreak context, and describe how this work is influencing thinking about COVID-19 vaccine efficacy trials. Emphasis is on smart placement of clinical trial sites, which can be informed both by surveillance and ensemble forecast modeling, and on multi-country strategies that are robust to unpredictable epidemic dynamics.

    This event is part of the DSI’s Decoding Pandemic Data: A Series of Interactive Seminars.

    More about Natalie Dean

    Biology, Medicine, Public Health
  • Nicholas Jewell, London School of Hygiene & Tropical Medicine; University of California, Berkeley

    The COVID-19 pandemic has allowed us to be reacquainted with properties of exponential growth and the role of epidemiological and statistical modeling as tools for policy. Dr. Jewell will briefly discuss some of these issues and point to the current statistical issues/studies as we try to look forward.

    This event is part of the DSI’s Decoding Pandemic Data: A Series of Interactive Seminars.

    More about Nicholas Jewell

    Biology, Medicine, Public Health
  • Join the Carney Institute for Brain Science for a conversation about the roots of addiction with Karla Kaun, Robert and Nancy Carney Assistant Professor of Neuroscience at Brown University.

    This event will be moderated by Diane Lipscombe, Reliance Dhirubhai Ambani Director of the Carney Institute, and Christopher Moore, Carney’s associate director.

  • Join the Carney Institute for Brain Science for a conversation about cognitive development in adolescence with Beatriz Luna, Staunton Professor of Psychiatry and Pediatrics at the University of Pittsburgh. 

    This event will be moderated by Diane Lipscombe, Reliance Dhirubhai Ambani Director of the Carney Institute, and Christopher Moore, Carney’s associate director.

  • Elizabeth Ogburn, Johns Hopkins

    A collaboration platform to facilitate aggregating evidence across COVID-19 RCTs

    The COVID-19 Collaboration Platform (CovidCP; https://covidcp.org) is a repository of RCT protocols whose PIs are open to collaborating with new teams of researchers in order to get high-quality evidence to clinicians quickly. Hundreds of COVID-19 interventional trials are currently registered on clinicaltrials.gov—and that number is growing quickly. Only a few of them have been centrally coordinated or are being run as multi-site trials, despite the fact that many of them are designed to answer similar questions. Sharing protocols and aggregating evidence across study sites could dramatically increase their efficiency and precision, getting answers to clinicians faster and more reliably. Furthermore, local outbreaks may taper off before individual institutions are able to enroll their target sample size. Notably, this has happened in China, where many trials are effectively suspended with incomplete enrollment and inconclusive results. If protocols are public and open for collaboration, an RCT can be picked up in different regions as the outbreak moves across the country.

    Many platforms currently exist for tracking COVID-19 trials and for sharing COVID-19 observational data, but none of them facilitate the formation of collaborations at all stages of research, and none of the existing data-sharing platforms target RCTs. The COVID-19 Collaboration Platform fills this gap by bringing together researchers who are interested in the same clinical questions at all stages of research. PIs who submit their protocols are encouraged and provided with support to:

    • share protocols with other research teams to create a multi-site collaborative protocol;
    • ad new sites under an existing PI and IRB;
    • share anonymized interim and/or final data with other sites that, independently, choose to operate a trial under a similar protocol; and
    • aggregate data and evidence across all sites studying similar interventions.
  • May
    1
    12:00pm

    CANCELED: DSCoV: Large Scale Deep Learning

    164 Angell Street

    Deep Learning provides recent and powerful ways to automate certain high level behaviors such as driving a car. How to create this new kind of real-world AI systems using massive amount of data? We will first review some data collection and labeling methods, computing infrastructures and softwares, statistical models and optimization algorithms. We will then present some coding principles and an experimental interface based on Python and Pytorch to sustain the development of these systems.

    Instructor: Remi Cadene

  • Join the Carney Institute for Brain Science for a conversation about the link between loss of smell and COVID-19 with Brown University professors Gilad Barnea and Alexander Fleischmann, who are experts in olfaction. 

    This event will be moderated by Diane Lipscombe, Reliance Dhirubhai Ambani director of the Carney Institute, and Christopher Moore, Carney’s associate director.

  • Apr
    21

    Join us at 4 PM on Tuesday, April 21 for a videoconference discussion of COVID-19 data, using the following Zoom link: https://brown.zoom.us/j/91357528021

    We will also use a chat system called Babylon House for facilitating the discussion. You can go ahead and join that classroom here: https://babylon.house/projects/9282c066-ff77-46d2-8a63-394af6ff5105/join 

    We will explore publicly available data sets and discuss some of the challenges inherent in drawing inferences and making predictions. The discussion will be accessible, introducing the data analysis tools we’ll use along the way. Hosted by Samuel Watson, the Director of Graduate Studies in the Data Science Initiative.

    Sign up here (optional): https://airtable.com/shrCltRlXys7sMZxE

  • Apr
    17
    The success of supervised machine learning in recent years crucially hinges on the availability of large-scale and unbiased data, which is often time-consuming and expensive to collect. Recent advances in deep learning focus on learning invariant representations that have found abundant applications in both domain adaptation and algorithmic fairness. However, it is not clear what price we have to pay in terms of task utility for such universal representations. In this talk, I will discuss my recent work on understanding and learning invariant representations.

    In the first part, I will focus on understanding the costs of existing invariant representations by characterizing a fundamental tradeoff between invariance and utility. In particular, I will use domain adaptation as an example to both theoretically and empirically show such tradeoff in achieving small joint generalization error. This result also implies an inherent tradeoff between fairness and utility in both classification and regression settings. In the second part of the talk, I will focus on designing learning algorithms to escape the existing tradeoff and to utilize the benefits of invariant representations. I will show how the algorithm can be used to guarantee equalized treatment of individuals between groups, and discuss what additional problem structure it requires to permit efficient domain adaptation through learning invariant representations.
    Han Zhao is a PhD candidate at the Machine Learning Department, Carnegie Mellon University, advised by Geoffrey J. Gordon. Before coming to CMU, he obtained his BEng degree in Computer Science from Tsinghua University (honored as a Distinguished Graduate) and MMath degree in mathematics from the University of Waterloo (honored with the Alumni Gold Medal Award). He has also spent time at Huawei Noah’s Ark Lab, Baidu Research, Microsoft Research, and the D. E. Shaw Group. His research interests are broadly in machine learning, with a focus on invariant representation learning and tractable probabilistic reasoning.
    Host: Professor Stephen Bach
  • Title and abstract TBA.

    This event has been rescheduled from 2/12 to 4/01. 

  • Nowadays the technology to create videogames and impressive photorealistic scenes is for everyone to reach. Frameworks such as Unity 3D Engine are easy to understand and work with or without any coding experience. So Why not using it to visualize data? You will learn basics on game objects components, UI design and concepts of volume rendering. Just bring your laptop with Unity Engine installed.

  • Even in the age of big data and machine learning, human knowledge and preferences still play a large part in decision making. For some tasks, such as predicting complex events like recessions or global conflicts, human input remains a crucial component, either in a standalone capacity or as a complement to algorithms and statistical models. In other cases, a decision maker is tasked with utilizing human preferences to, for example, make a popular decision over an unpopular one. However, while often useful, eliciting data from humans poses significant challenges. First, humans are strategic, and may misrepresent their private information if doing so can benefit them. Second, when decisions affect humans, we often want outcomes to be fair, not systematically favoring one individual or group over another.

     In this talk, I discuss two settings that exemplify these considerations. First, I consider the participatory budgeting problem in which a shared budget must be divided among competing public projects. Building on classic literature in economics, I present a class of truthful mechanisms and exhibit a tradeoff between fairness and economic efficiency within this class. Second, I examine the classic online learning problem of learning with expert advice in a setting where experts are strategic and act to maximize their influence on the learner. I present algorithms that incentivize truthful reporting from experts while achieving optimal regret bounds.

    Rupert Freeman is a postdoc at Microsoft Research New York City. Previously, he received his Ph.D. from Duke University under the supervision of Vincent Conitzer. His research focuses on the intersection of artificial intelligence and economics, particularly in topics such as resource allocation, voting, and information elicitation. He is the recipient of a Facebook Ph.D. Fellowship and a Duke Computer Science outstanding dissertation award.
    Host: Professor Amy Greenwald
  •  

    This workshop will provide a high-level overview of the main areas in machine learning with emphasis on supervised and unsupervised ML. We will review the strengths and limitations of ML, describe the bias-variance tradeoff which is one of the most important concepts in ML. Finally, we will review the main steps in a typical ML workflow and the most common problems to avoid.

  • Some claim AI is the “new electricity” due to its growing significance and ubiquity. My research investigates this vision from an HCI perspective: How can we situate this remarkable technology in ways people perceive as valuable? How could we form a symbiotic relationship between systems that utilize machine learning and their users, to do things neither can do on their own?

    In this talk, I will discuss a number of research projects that systematically investigate these questions. Projects include the designs of clinical decision-support systems that can effectively collaborate with doctors in making life-and-death decisions and an investigation of how Natural Language Generation systems might seamlessly serve authors’ communicative intent. Each project engages stakeholders in their real-world contexts and addresses a critical challenge in transitioning AI from the research lab to the real world.

    Based upon this body of work and my studies of industry practice, I propose a framework laying out the problem space of human-AI interaction design. I discuss our early work in understanding AI as a material for HCI design and synthesizing generalizable design methods.

    Qian Yang is a Human-Computer Interaction (HCI) researcher and a Ph.D. candidate at the School of Computer Science at Carnegie Mellon University. Her research draws together theories and methods from design, the social sciences, and machine learning to advance human-AI interaction. She is best known for designing decision support systems that effectively aided physicians in making critical clinical decisions.

    Her work has been supported by the National Institute of Health, the National Science Foundation, and the Department of Health and Human Services. During her Ph.D., she has collaborated with researchers at Google Brain, Microsoft Research, among others. She published fifteen peer-reviewed publications on the topic of human-AI interaction at premiere HCI research venues. Four of these won paper awards. She is the recipient of a Digital Health fellowship from the Center for Machine Learning and Health, a Microsoft Research Dissertation Grant, and an Innovation by Design Award from FastCompany. Her work was featured on various global media outlets. This spring she will be speaking at SXSW on designing AI products and services.

    Host: Professor Jeff Huang
  • Mar
    13

    GitHub Actions is a relatively new service by GitHub that allows you to run tests, deploy code, and more all from within your repo. This workshop will cover some of the basic use cases of GitHub Actions as well as show some of the more creative ways it can be used to automate your workflows. Attendees should be familiar with Git and GitHub.

     

    Instructor: Mary McGrath 

  • Sumithra Mandrekar, PhD
    Mar
    9
    3:00pm - 4:00pm

    Statistics C. V. Starr Lecture: Sumithra J. Mandrekar, PhD

    121 South Main Street

    Sumithra J. Mandrekar, PhD, Professor of Biostatistics and Oncology, Mayo Clinic

    Clinical Trial Designs for Personalized Medicine in Oncology

    Clinical trial design strategies have evolved as a means to accelerate the drug development process so that the right therapies can be delivered to the right patients. Basket, umbrella, SMART and adaptive enrichment strategies represent a class of novel designs for testing targeted therapeutics, and individualizing treatment in oncology. Umbrella trials include a central infrastructure for screening and identification of patients, and focus on a single tumor type or histology with multiple sub trials, each testing a targeted therapy within a molecularly defined subset. Basket trial designs offer the possibility to include multiple molecularly defined subpopulations, often across histology or tumor types, but included in one cohesive design to evaluate the targeted therapy in question. Adaptive enrichment designs offer the potential to enrich for patients with a particular molecular feature that is predictive of benefit for the test treatment based on accumulating evidence from the trial. A SMART design strategy is appropriate when comparing adaptive interventions, which are defined by an individual’s sequence of treatment decisions and guided by intermediate outcomes, such as response to therapy. This talk will focus on the fundamentals of these design strategies, the underlying statistical framework, the logistical barriers of implementation, and, ultimately, the interpretation of the trial results, using some case-studies including the National Cancer Institute’s precision medicine initiative trials.


    Bio: 

    Dr. Mandrekar received her interdisciplinary Ph.D. in Biostatistics, Psychology, Internal Medicine and Biomedical Engineering from the Ohio State University, Columbus OH in December 2002, and joined Mayo Clinic as a research associate in January 2003. She is currently Professor of Biostatistics and Oncology at the Mayo Clinic, Rochester MN, and Section Head for the Cancer Center Statistics within Mayo Clinic Department of Health Sciences Research. She is the Group Statistician for the Alliance for Clinical Trials in Oncology, which is one of the 4 NCI-funded national clinical trials networks for the conduct of phase II and III clinical trials in adult cancer. Dr. Mandrekar also holds an adjunct Professor of Biostatistics appointment at the School of Public Health, University of Minnesota, and at the University of Gainesville, FL.

    Dr. Mandrekar’s primary research interests include adaptive dose-finding early phase trial designs, designs for predictive biomarker validation, and general clinical trial methodology related to conduct of clinical trials and identification of alternative Phase II cancer clinical trial endpoints. Dr. Mandrekar has co-authored over 140 original papers; several book chapters and editorials; and has given numerous lectures, invited presentations and workshops on these topics.

    Her primary collaborative areas are lung cancer and leukemia. She is the faculty statistician for the national adjuvant lung cancer trial, ALCHEMIST, an approximately 8000 patient trial, which is part of the NCI precision medicine initiative. She was the primary statistician on the Phase III C10603 trial that led to the FDA approval of Midostaurin for AML patients with FLT3 mutations.

    Dr. Mandrekar is a voting member of the NCI thoracic malignancies steering committee, past president of the society for clinical trials (2018-2019), voting member on the clinical trials transformation initiative on master protocols, member of the ASCO mCODE executive council, voting member of the international RECIST working group and the biostatistics editor for the Journal of Thoracic Oncology.

    She has a national and international presence as an expert in clinical trial design as evidenced by her bibliography and membership on various national and international committees.

    Biology, Medicine, Public Health, BioStatsSeminar, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Training, Professional Development
  • Mar
    6
    12:00pm

    DSCoV Workshop: Intro to PyTorch

    164 Angell Street

    Intro to PyTorch, by Minju Jung

     

    Nowadays, PyTorch is one of the most popular deep learning libraries. In this tutorial, I will cover the basic operations of PyTorch and building a neural network model for image classification. The basic knowledge of deep learning and coding experience with python are required.

  • Mar
    5
    12:00pm - 1:00pm

    Introduction to LaTeX

    Sciences Library, 3rd floor

    Are you interested in learning more about LaTeX? The LaTeX Workshop series, run through the Science Center, aims to teach how to effectively use LaTeX, a document preparation system used to typeset technical and scientific documents.

    Each workshop integrates lecture time along with time for students to apply what they have learned through exercises and students will develop a template document over the course of each workshop. We offer workshops at the beginning and intermediate levels. Participants may register for any level in the series, but prior knowledge is expected for the intermediate workshops. Registration is now open for our first workshop, “Introduction to LaTeX & Math”.

    Lunch is provided.

    Academic Calendar, University Dates & Events, Education, Teaching, Instruction, Mathematics, Technology, Engineering
  • Generative neural networks for population genetics: Creating artificial genomes

    Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create realistic synthetic data is still under-exploited in genetics and absent from population genetics. Yet a known limitation of this field is the reduced access to many genetic databases due to concerns about violations of individual privacy, although they would provide a rich resource for data mining and integration towards advancing genetic studies. We demonstrated that deep generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs) can be trained to learn the high dimensional distributions of real genomic datasets and generate novel high- quality artificial genomes (AGs) with little privacy loss. We show that our generated AGs replicate characteristics of the source dataset such as allele frequencies, linkage disequilibrium, pairwise haplotype distances and population structure. Moreover, they can also inherit complex features such as signals of selection and genotype-phenotype associations. To illustrate the promising outcomes of our method, we showed that imputation quality for low frequency alleles can be improved by augmenting reference panels with AGs and that the RBM latent space provides a relevant encoding of the data, hence allowing further exploration of the reference dataset and providing features that could help solving supervised tasks.

  • Data Science Computation and Visualization Workshop

     

    EXPLORATORY DATA ANALYSIS WITH PANDAS IN PYTHON, PART TWO with Andras Zsom

    Exploratory data analysis (EDA) is the first step of any data science project. In the second part of this pandas tutorial, I’ll walk through various visualization types you can use to better understand the properties of your data at a glance using pandas. Coding experience with python is required but no experience with the pandas package is necessary to follow the tutorial.

     

    Friday 2/28 @ 12pm

    Carney Innovation Space, 4th Floor

    164 Angell Street

    Pizzas and sodas will be served. Sponsored by the Data Science Initiative and organized by the Center for Computation and Visualization.

  • Feb
    27
    3:00pm

    DSI Special Seminar: Richard Li (Yale)

    121 South Main Street

    METHODS FOR POPULATION HEALTH WITH LIMITED DATA 

    ZEHANG (RICHARD) LI, 
    PhD Postdoctoral Research Associate
    Department of Biostatistics, Yale University

     

    Data describing health outcomes of hidden populations and in low-resource areas are usually noisy and incomplete. In this talk, I will discuss two projects in such data-constrained settings. In the first project, I propose probabilistic approaches to estimating cause of death distributions using verbal autopsies (VA). VA is a survey-based method that is routinely carried out to assign causes to deaths when medically certified causes are not available. I will present an approach to use latent Gaussian graphical models to characterize the joint distribution of symptoms and causes of death while accommodating informative prior knowledge about their marginal associations. This allows us to combine noisy data from multiple sources to improve the cause of death classification. I will also briefly discuss the broader impact of probabilistic modeling of VA based on pilot studies to integrate VA with existing civil registration system.In the second project, I will discuss methods to evaluate population-level public health interventions for combating the opioid epidemics. Opioid use and overdose has become an important public health issue in the United States. However, understanding the dynamics of opioid overdose incidents and effects of public health interventions remains challenging, as comprehensive datasets describing drug use usually lack. I will discuss challenges in evaluating impacts of spatially- and time-varying exposures with unmeasured confounding and spillover effects. I will discuss methods to leverage the space-time structures to adjust for certain types of confounding due to smooth latent processes and develop strategies to evaluate the sensitivity of such adjustments.


    Zehang (Richard) Li is currently a postdoctoral associate in the Department of Biostatistics at Yale School of Public Health. He completed his PhD in Statistics at the University of Washington in 2018.His research interests include Bayesian hierarchical models for high-dimensional data, spatial-temporal statistics, causal inference, global and population health, and reproducible research.

  • Feb
    26

    At the Interface of Data and Decision Making Under Uncertainty

    In this talk I will give an overview of two ongoing projects at the interface of data and stochastic simulation/optimization models. I will first discuss our work on “stochastic package queries” (SPQs), a framework for synthesizing data management systems, predictive models, and optimization tools to provide an end-to-end system for decision support in the face of uncertainty. The goal of a SQP is to select a subset of tuples in a table (e.g., a portfolio of stocks) to optimize the expected value of an aggregate over the subset, while satisfying a set of constraints with high probability. We use a Monte Carlo database system to incorporate stochastic predictive models into the database and provide a declarative extension to SQL, called sPaQL, for specifying SPQs. Prior stochastic programming approaches typically do not scale to the SPQ setting, so we provide novel techniques for scalably computing approximately optimal solutions with statistical guarantees, while not overwhelming optimization engines such as CPLEX and Gurobi.

     

    Our second project aims at the ultimate goal of automatically generating and executing discrete-event stochastic simulation (DESS) models to seamlessly incorporate expert domain knowledge into decision making under uncertainty for complex dynamic systems. Perhaps the most challenging step in the creation of a DESS model is specification of the input distributions, e.g., for the arrival process in a queueing model. Traditionally, small amounts of historical data would be available; distribution-fitting software would assume that interarrival times are iid, and then select and parameterize one of a small family of standard distributions, such as exponential, gamma, or Weibull. We show that such software often fails for processes with complex features such as multi-modal marginal distributions or temporal correlations. We design novel generative neural networks, specifically, variational autoencoders with LSTM components, that permit automated, higher fidelity simulation input modeling in data-rich scenarios. Preliminary results show that a range of complex processes can be automatically and accurately modeled by our techniques, without overfitting.

    Peter J. Haas is a Professor of Information and Computer Sciences at the University of Massachusetts Amherst, as well as an Adjunct Professor in Mechanical and Industrial Engineering. From 1987-2017 he worked at the IBM Almaden Research Center, rising to the level of Principal Research Staff Member. He was also a Consulting Professor in the Department of Management Science and Engineering at Stanford University from 1992-2017. His research interests lie at the interface of information management, applied probability, statistics, and computer simulation. He has worked on topics including modeling, stability, and simulation of discrete-event stochastic systems, approximate query processing, the Splash platform for collaborative modeling and simulation, techniques for massive-scale data analytics (matrix completion, dynamic graph analysis, and declarative machine learning), Monte Carlo methods for scalable querying and Bayesian learning over massive uncertain data, automated relationship discovery in databases, query optimization methods, autonomic computing and visualization recommendation systems. He was designated an IBM Master Inventor in 2012, and his ideas have been incorporated into products including IBM’s DB2 database system. He is a Fellow of both ACM and INFORMS, and has received a number of awards from IBM and both the Simulation and Computer Science communities, including an IBM Research Outstanding Innovation Award, VLDB 2016 Best Paper Award, an ACM SIGMOD 10-year Best Paper award, and an INFORMS Simulation Society Outstanding Simulation Publication Award for his monograph on stochastic Petri nets. He serves on the editorial boards of ACM Transactions on Database Systems and ACM Transactions on Modeling and Computer Simulation, and was an Associate Editor for the VLDB Journal from 2007 to 2013 and for Operations Research from 1995-2017. He is the author of over 130 conference publications, journal articles, and books, and has been granted over 30 patents.
  • Feb
    26

    I will describe how to use data science methods to understand and reduce inequality in two domains: criminal justice and healthcare. First, I will discuss how to use Bayesian modeling to detect racial discrimination in policing. Second, I will describe how to use machine learning to explain racial and socioeconomic inequality in pain.

    Emma Pierson is a PhD student in Computer Science at Stanford, supported by Hertz and NDSEG Fellowships. Previously, she completed a master’s degree in statistics at Oxford on a Rhodes Scholarship. She develops statistical and machine learning methods to study two deeply entwined problems: reducing inequality and improving healthcare. She also writes about these topics for broader audiences in publications including The New York Times, The Washington Post, FiveThirtyEight, and Wired. Her work has been recognized by best paper (AISTATS 2018), best poster (ICML Workshop on Computational Biology), and best talk (ISMB High Throughput Sequencing Workshop) awards, and she has been named a Rising Star in EECS and Forbes 30 Under 30 in Science.

    Host: Professor Seny Kamara

  • Feb
    26
    12:00pm

    DSI Special Seminar: John Laudun

    Rockefeller Library

    “Are We Not Doing Phrasing Anymore?”: Towards a Cultural Informatics

    John Laudun, University of Louisiana

    Recent headlines in major news outlets like the New York Times or the Chronicle of Higher Education reveal the profound suspicion with which statistical methods have been received within the humanities. The pervasive belief is that a chasm lies between statistics and the humanities that not only cannot be bridged but should not be attempted, at the risk of losing the human. And yet slowly and steadily a growing number of practitioners have not only developed research programs but also pedagogical methods that open up new analytical perspectives as well as new avenues for students to explore their relationship between the subject matter and their own understanding. This talk offers a small survey of various practices to be found in the digital humanities alongside a few experiments by the author in allowing students to experience how statistical methods in fact de-mystify the meaning-making process in language and empower students not only to ground their insights in things they can see, and count, but also, in understanding texts as nothing more than certain sequences of words, opening a path to making them better writers as well. Working from a broad survey to narrow applications, the talk suggests that concerns about a loss of humanity in the humanities is actually a concern for loss of certain kinds of authority, but that new kinds of authority are possible within which researchers and teachers will find a firm ground from which to offer interpretations and evaluations of the kinds of complex artifacts that have long been the purview of the domain.

    John Laudun received his MA in literary studies from Syracuse University in 1989 and his PhD in folklore studies from the Folklore Institute at Indiana University in 1999. He was a Jacob K. Javits Fellow while at Syracuse and Indiana (1987-1992), and a MacArthur Scholar at the Indiana Center for Global Change and World Peace (1993-94). He has written grants that have been funded by the Grammy Foundation and the Louisiana Board of Regents, been a fellow with the EVIA Digital Archive and a scholar in residence with UCLA’s Institute for Pure and Applied Mathematics. His book, The Amazing Crawfish Boat, is a longitudinal ethnographic study of creativity and tradition within a material folk culture domain. Laudun’s current work is in the realm of culture analytics. He is currently engaged in several collaborations with physicists and other scientists seeking to understand how texts can be modeled computationally in order to better describe functions and features.

    Lunch will be served!

  • Big data for small earthquakes: Computational challenges in large-scale earthquake detection

     

    Earthquake detection – the identification of weak earthquake signals in continuous waveform data recorded by sensors in a seismic network – is a fundamental task in seismology. In this talk, I will describe the data science challenges associated with earthquake detection in massive seismic data sets. I will discuss how new algorithmic advances in machine learning and data mining are helping to advance the state-of-the-art in earthquake monitoring.

    As a case study, I will present Fingerprint and Similarity Thresholding (FAST), a novel method for large-scale earthquake detection inspired by audio recognition technology (Yoon et al., 2015). FAST uses locality-sensitive hashing, a technique for efficiently identifying similar items in large data sets, to detect similar waveforms (candidate earthquakes) in continuous seismic data. By posing earthquake detection as a data mining problem, FAST can discover new earthquake sources without training data, which is often unavailable for seismic data sets. FAST has recently been extended to long-duration, multi-sensor seismic data sets (Bergen and Beroza, 2018; Rong et al., 2018; Yoon et al., 2019) – introducing a capability for large-scale unsupervised detection that was not previously available for seismic data analysis.

     

    The latest generation of earthquake detection methods, including FAST and other new approaches based on deep neural networks, reflect a broader trend toward data-driven methods in the solid Earth geosciences (Bergen et al., 2019). I will conclude the talk with a brief discussion of opportunities for collaboration between the geoscience and data science communities that will advance the state-of-the art in both fields. In particular, I will highlight how research in the emerging discipline of scientific machine learning will play a critical role in driving discovery in the Earth and physical sciences.

     

    Dr. Karianne J. Bergen is a Data Science Initiative Postdoctoral Fellow in Computer Science at Harvard University’s John A. Paulson School of Engineering and Applied Sciences. Her research focuses on the use of machine learning for pattern recognition and discovery in noisy, real-world scientific data sets. For her doctoral research, she developed a new algorithm for automatically identifying weak earthquake signals in large seismic data sets. Her research has been recognized with awards from the Seismological Society of America, the American Geophysical Union and the Royal Astronomical Society. Prior to her graduate studies, Dr. Bergen worked as a staff data scientist at MIT-Lincoln Laboratory. She holds a M.Sc. and Ph.D. in Computational and Mathematical Engineering from Stanford University and a B.Sc. in Applied Mathematics from Brown University.

  • Feb
    25
    12:00pm

    DSI Tech Lunch: Even Shen (Minted)

    164 Angell Street


    Brown University DSI MS graduate, Even Shen, is a Data Analyst at Minted. Minted is a design marketplace, sourcing creative content from independent artists around the globe and selling the best designs to the world in the form of art, home decor, and stationery. Even’s talk will cover some information about how Minted got started & the products they offer as well as going into depth about her role within the company and her journey from studying at Brown through becoming a Data Analyst at Minted.

    Must have a valid Brown ID to enter 164 Angell Street, 3rd Floor. 

  • Feb
    21

    Instructor: Rex Liu

    This tutorial will provide a crash course on some of the basic methods in reinforcement learning. No prior knowledge beyond Python will be assumed. Emphasis will be on methods rather than proofs. We shall begin with Markov decision processes, the framework upon which all RL is formulated, followed by the central equation that RL essentially attempts to optimise, the Bellman equation. We shall then discuss how all learning methods attempt to optimise this equation, namely through policy evaluation, policy improvement, and value iteration. Finally, we shall cover one of the most important families of RL algorithms, TD-learning, and provide some hands-on exercises to play with these algorithms.

    Carney Innovation Space, 4th Floor

    164 Angell Street

    Pizzas and sodas will be served. Sponsored by the Data Science Initiative and organized by the Center for Computation and Visualization.

  • Feb
    19
    4:00pm

    Data Wednesday: Aaron Sams (emBark)

    164 Angell Street

    RUNS OF HOMOZYGOSITY, RECESSIVE DISEASE GENOTYPES, AND INBREEDING DEPRESSION IN DOMESTIC DOGS

     

    AARON SAMS
    SENIOR SCIENTIST, EMBARK VETERINARY INC.

     

    Inbreeding leaves distinct genomic traces, most notably long genomic tracts that are identical by descent and completely homozygous. These runs of homozygosity (ROH) can contribute to inbreeding depression if they contain deleterious variants that are fully or partially recessive. The aim of this study was to examine the relationship between inbreeding measured from ROH the severity of inbreeding depression in several breeds for which phenotype data was available.

    We examined genome-wide data from over 200,000 markers, which we used to build high-resolution ROH density maps for over 3,000 dogs, recording ROH down to 500 kilobases. Additionally, we utilized reproductive fitness-related phenotype data from the Morris Animal Foundation’s Golden

    Retriever Lifetime Study, Doberman Pinscher longevity data from the Doberman Diversity Project, and other phenotype data collected from Embark Veterinary customers. We find that over the range of coefficient of inbreeding (COI) levels observed within several dog breeds, inbreeding depression is clearly evident using various types of phenotypic measures.

    In most breeds, genetic testing to reduce the incidence of high COI litters will have a greater impact on animal welfare and population health than the current testing of known Mendelian disorders recommended by breed clubs.

     

     

    Aaron Sams received his Master of Science in Biological Anthropology in 2010, as well as his Doctorate in Philosphy in 2012, both from the University of Wisconsin-Madison.

    After spending almost four years post- graduation in Madison, Aaron went on to be a postdoctoral researcher at Cornell University before settling in at Embark, where he has been for almost four years.

    He is an experienced interdisciplinary scientist with a background in biological anthropology, genomics, and computational biology. Aaron has a demonstrated history of working in the veterinary industry and uses computational methods to understand problem sin human evolutionary genomics. He is now applying those skills to canine genomics to help improve the lives of dogs.

  • Adapting black-box machine learning methods for causal inference

    I’ll discuss the use of observational data to estimate the causal effect of a treatment on an outcome. This task is complicated by the presence of ‘confounders’ that influence both treatment and outcome, inducing observed associations that are not causal. Causal estimation is achieved by adjusting for this confounding by using observed covariate information. I’ll discuss the case where we observe covariates that carry sufficient information for the adjustment, but where explicit models relating treatment, outcome, covariates, and confounding are not available. For example, in medical data the covariates might consist of a large number of convenience health measurements of which only an unknown subset are relevant, and even then in some totally unknown manner. Or, the covariates might be a passage of (natural language) text that describes the relevant information. I’ll describe how to modify standard architectures and training objectives to achieve statistically efficient and practically useful causal estimates, as well as how to adapt traditional approaches to evaluating sensitivity to unobserved confounding to allow for the use of blackbox models.

    Victor Veitch is a distinguished postdoctoral research scientist in the department of statistics at Columbia University. He completed his PhD in Statistics at the University of Toronto. His work addresses both the use of machine learning for causal inference, and the modeling of relational and network data. He has been recognized with a number of awards, including the 2017 Pierre Robillard award for best Statistics PhD thesis in Canada.

    Mathematics, Technology, Engineering
  • Feb
    18

    Inverse Problems, Imaging and Tensor Decomposition

    Perspectives from computational algebra and non-convex optimization are brought to bear on a scientific application and a data science application. In the first part of the talk, I will discuss cryo-electron microscopy (cryo-EM), an imaging technique to determine the 3-D shape of macromolecules from many noisy 2-D projections, recognized by the 2017 Chemistry Nobel Prize. Mathematically, cryo-EM presents a particularly rich inverse problem, with unknown orientations, extreme noise, big data and conformational heterogeneity. In particular, this motivates a general framework for statistical estimation under compact group actions, connecting information theory and group invariant theory. In the second part of the talk, I will discuss tensor rank decomposition, a higher-order variant of PCA broadly applicable in data science. A fast algorithm is introduced and analyzed, combining ideas of Sylvester and the power method.


    Joe Kileel is currently a Simons Postdoctoral Research Associate in the Program in Applied and Computational Mathematics, Princeton University, working with Amit Singer’s group. In 2017, he received a Mathematics PhD from UC Berkeley under the supervision of Bernd Sturmfels, where his thesis was awarded the Bernard Friedman Memorial Prize for best in applied mathematics. Joe’s research interests center on mathematical data science, with focuses on imaging science, tensor methods, computational statistics and inverse problems. He is especially interested in the development of scalable and robust nonlinear algebraic techniques for scientific computing and data
    science.

  • Title: Networking for Big Data: Theory, Algorithms and Applications

     

    Edmund Yeh, PhD
    Professor of Electrical and Computer Engineering
    College of Engineering
    Khoury School of Computer Science
    Northeastern University

     

    In the era of big data, domain experts in various science and engineering fields are facing unprecedented challenges in global data distribution, processing, access and analysis, and in the coordinated use of limited computing, storage and network resources. To meet this challenge and to close an underlying gap between the needs of data-intensive applications and the structure of today’s networks, data-centric network architectures such as Named Data Networking (NDN) have been proposed, which focus on enabling end users to obtain the data they want, rather than to communicate with specific nodes.

    In this talk, we present new frameworks for the optimization of key functionalities supported by data-centric networking, which are broadly applicable to data-intensive science networks, 5G wireless edge networks, and content delivery networks. The frameworks enable the joint optimization of (in-network) caching and traffic engineering for data distribution, as well as joint computation scheduling, caching and request forwarding for distributed computing. We discuss two classes of distributed and adaptive algorithms for the joint optimizations, one based on throughput optimal control and another based on convex relaxation and stochastic gradient ascent. We analytically provide optimality guarantees for the algorithms in terms of relevant performance metrics, and show that the state-of-the-art algorithms significantly outperform baseline policies over a broad array of network settings.

    Finally, we discuss results from a recent landmark demonstration which implements the optimization frameworks and algorithms to accelerate data distribution in the Large Hadron Collider (LHC) high-energy physics network, one of the largest data applications in the world.

     

    Edmund Yeh received his B.S. in Electrical Engineering with Distinction and Phi Beta Kappa from Stanford University in 1994. He then studied at Cambridge University on the Winston Churchill Scholarship, obtaining his M.Phil in Engineering in 1995. He received his Ph.D. in Electrical Engineering and Computer Science from MIT under Professor Robert Gallager in 2001. He is currently Professor of Electrical and Computer Engineering at Northeastern University. He was previously Assistant and Associate Professor of Electrical Engineering, Computer Science, and Statistics at Yale University. He has held visiting positions at MIT, Stanford, Princeton, UC Berkeley, NYU, EPFL, and TU Munich.

    Professor Yeh was one of the PIs on the original NSF-funded FIA Named Data Networking project. He is the recipient of the Alexander von Humboldt Research Fellowship, the Army Research Office Young Investigator Award, the Winston Churchill Scholarship, the National Science Foundation and Office of Naval Research Graduate Fellowships, the Barry M. Goldwater Scholarship, the Frederick Emmons Terman Engineering Scholastic Award, and the President’s Award for Academic Excellence (Stanford University). Professor Yeh has received three Best Paper Awards, including awards at the 2017 ACM Conference on Information-Centric Networking (ICN), and at the 2015 IEEE International Conference on Communications (ICC) Communication Theory Symposium. He will serve as General Chair for ACM SIGMETRICS 2020 and TPC Co-Chair for ACM MobiHoc 2021.

  • Data Science Computation and Visualization Workshop

     

    EXPLORATORY DATA ANALYSIS WITH PANDAS IN PYTHON, PART ONE with Andras Zsom

     Exploratory data analysis (EDA) is the first step of any data science project. In the first part of this pandas tutorial, I’ll walk through how to read in csv, excel, and sql data into a pandas data frame, how to select specific rows and columns based on index or condition, and how to merge and append various data frames. Coding experience with python is required but no experience with the pandas package is necessary to follow the tutorial.

     

    Friday 2/14 @ 12pm

    Carney Innovation Space, 4th Floor

    164 Angell Street

    Pizzas and sodas will be served. Sponsored by the Data Science Initiative and organized by the Center for Computation and Visualization.

  • Feb
    14
    8:30am - 10:00am

    Carney Coffee Hour with Data Specialists

    164 Angell Street
    Do you have questions about data sharing, retention, curation, access, or management?
    As part of Love Data Week, please join the Carney Institute for a special Carney Coffee Hour and informal chat with data specialists from the Office of Research Integrity:
    Keri Godin, Senior Director of the Office of Research Integrity
    Andrew Creamer, Scientific Data Management Specialist
    Arielle Nitenson, Senior Research Data Manager
    Biology, Medicine, Public Health, Education, Teaching, Instruction, Libraries, Psychology & Cognitive Sciences, Research
  • Feb
    13
    4:00pm - 5:30pm

    Challenging Silicon Valley’s Infinite Loop of Irresponsibility: Natasha Singer, NYT

    Building for Environmental Research and Teaching (BERT), 85 Waterman Street

    Brown alumna and New York Times reporter Natasha Singer will talk about a new movement – “responsible computing”

    The medical profession has an ethic: First, do no harm, Silicon Valley has an ethos: Build it first and ask for forgiveness later. We’re increasingly seeing the consequences of that move-fast ethos: election interference, mass disinformation and the incitement of ethnic hatred. In this talk, Natasha will discuss the techniques journalists use to examine the consequences of emerging technologies on society – reporting that often prompts reactive changes at tech companies. She’ll also talk about a new movement – “responsible computing” – that aims to embed the analysis of societal consequences in computer science.

    Brown alumna Natasha Singer is a reporter at The New York Times where she covers the intersection of technology, business and society, with a particular focus on data privacy, fairness and industry accountability. She was a member of a New York Times’ reporting team whose privacy coverage won a Polk Award in National Reporting in 2019 and was a finalist for a 2019 Pulitzer Prize in National Reporting. Her recent series for the Times, called “Education Disrupted,” uncovered how tech giants like Google and Microsoft are working to reshape public education. Her previous Times’ series on the consumer data industry, called “You For Sale,” helped prompt several congressional and federal investigations, as well as the enactment of student online data privacy laws in California and other states. Natasha also developed and teaches a course on responsible computing at The School Of The New York Times, the newspaper’s pre-college summer program for high school students.

    Reception to follow.

    Please register at: https://natashasinger-browncs.eventbrite.com

  • Gradient Flows: From PDE to Data Analysis

     

    Certain diffusive PDEs can be viewed as infinite-dimensional gradient flows. This fact has led to the development of new tools in various areas of mathematics ranging from PDE theory to data science. In this talk, we focus on two different directions: model-driven approaches and data-driven approaches. In the first part of the talk we use gradient flows for analyzing non-linear and non-local aggregation-diffusion equations when the corresponding energy functionals are not necessarily convex. Moreover, the gradient flow structure enables us to make connections to well-known functional inequalities, revealing possible links between the optimizers of these inequalities and the equilibria of certain aggregation-diffusion PDEs. In the second part, we use and develop gradient flow theory to design novel tools for data analysis. We draw a connection between gradient flows and Ensemble Kalman methods for parameter estimation. We introduce the Ensemble Kalman Sampler - a derivative-free methodology for model calibration and uncertainty quantification in expensive black-box models. The interacting particle dynamics underlying our algorithm can be approximated by a novel gradient flow structure in a modified Wasserstein metric which reflects particle correlations. The geometry of this modified Wasserstein metric is of independent theoretical interest.

    Franca Hoffmann is a von Karman instructor at California Institute of Technology. She completed her PhD at the Cambridge Centre for Analysis at University of Cambridge (UK) in 2017, supervised by Jose A. Carrillo and Clément Mouhot. Her research is focused on the applied mathematics/data analysis interface, driven by the need to provide rigorous mathematical foundations for modeling tools used in applications. In particular, Franca is interested in the theory of nonlinear and nonlocal PDEs, as well as in developing novel tools for data analysis and mathematical approaches to machine learning.

  • Feb
    6
    5:30pm - 6:30pm

    NEW Undergraduate Data Fluency Certificate Info Session

    Smith-Buonanno Hall

    The Data Science Initiative will be hosting an informational session discussing the new undergraduate Data Fluency Certificate on Thursday, Feb 6th from 5:30 – 6:30 p.m. in Smith Buonanno Hall, Room 101.

    The certificate is designed to meet the learning goals of students interested in data science, without undertaking a CS or related concentration.

    The certificate consists of three required courses, an upper level elective, and an experiential component. Current juniors, sophomores, and first year students are eligible for the Data Fluency Certificate. Students concentrating in any of the following (Computer Science, Applied Math, Computational Biology, Mathematics, or Statistics) (either jointly in combination) are not eligible.

    Please join us for the information session to learn more about this exciting new data science opportunity at Brown!

  • Feb
    6

    Please join the Carney Institute for Brain Science for an informal lunch with Brown alumna Allison Paradise. Allison will talk about her career path and how her BASc in Neuroscience helped her found My Green Labs, a non-profit working to reduce waste in scientific research labs. 

    Please RSVP to help with the head count for catering. 

    Biology, Medicine, Public Health, Careers, Recruiting, Internships, Graduate School, Postgraduate Education, Psychology & Cognitive Sciences, Training, Professional Development
  • Brown Center for Biomedical Informatics Presents Dr. Anita Crescenzi: Adaptation in Information S...
    Jan
    30
    3:00pm - 4:00pm

    Adaptation in Information Search and Decision-Making Under Time Pressure

    Brown University Medical Education Building (Alpert Medical School)

    Adaptation in Information Search and Decision-Making Under Time Pressure

    Dr. Anita Crescenzi

     

    In this talk, Dr. Crescenzi will summarize several studies that have investigated the effects of time limits and time pressure on search and decision-making behaviors. Dr. Crescenzi found evidence of different types of adaptation under time pressure from analysis of traces of users’ interactions with search systems, participant’s perceptions of their process, and task outcomes. Under time pressure, people may exhibit signs of one or more types of adaptation: they may adapt the search process (e.g., decide not to search, search more shallowly), adjust the search outcome (e.g., look at fewer pages of information), or adjust the decision outcome (e.g., make a less specific recommendation). The context in which the search and decision-making takes place influences the types of adaptation that are observed and even possible. Dr. Crescenzi will finish the talk by applying the findings to a clinical decision-making context.

    BIO: Anita Crescenzi is a Postdoctoral Research Associate at the School of Information and Library Science at the University of North Carolina at Chapel Hill (UNC). Anita received her Ph.D. in Information and Library Science in 2019. Her research interests include interactive information retrieval, human-computer interaction, and decision-making. She seeks to 1) understand how people use search systems to seek information to use in support of their broader goals, 2) design and evaluate novel search interaction features to better support learning, problem-solving, and decision-making, and 3) develop better measures of search behavior and learning during search. She has published her research at SIGIR, CHIIR, ICTIR, and ASIST. She also has eight years of industry and medical library experience in user experience design, usability evaluation, user research, and applications development. As the head of the applications development group, she led the design, development, and evaluation of the UNC Health Sciences Library website with over 1 million annual visits serving more than 10,000 health affairs faculty, staff, and students; 5,000 residents and staff of UNC Hospitals; and 2,000 clinical preceptors.

    Biology, Medicine, Public Health, Libraries, Research
  • Jan
    29
    4:00pm

    Data Wednesday: Achal Neupane

    164 Angell Street

    Title and abstract TBA.

  • PSTC is hosting a Getting Started with Python and Data Science workshop. This is an introductory two-day workshop that aims to provide participants with an immersive practice on Python and data analytics. The workshop is designed primarily for Python beginners, and no prior programming experience is required. After this workshop, the participants will be prepared to understand the basics of Python, install and use Python packages, develop Python scripts and manipulate and analyze data using computing packages (e.g., NumPy, Pandas). In addition, participants will become familiar with Python working environment, Anaconda and Jupyter Notebook.

    This workshop is open to students, postdocs, faculty and other researchers with a Brown University affiliation.

    When: Wed - Thu, January 15-16, 2020, 9:00 AM – 4:00 PM EST

    Where: Seminar Room (the 2nd floor of PSTC), 68 Waterman St, Providence, RI, 02912. Get directions with Google Maps.

    Survey: Pre-workshop survey

    Registration: Eventbrite

    Requirements: Please complete the pre-workshop survey before attending. Participants must bring a laptop with a Windows or Mac operating system that they have administrator permission on.

    Contact: Please email [email protected] or Zhenchao Qian with questions about the content or logistics..

  • Jan
    9
    12:00pm - 1:00pm

    Translational Research Seminar Series

    Rhode Island Quality Institute

    Join Advance-CTR for our January Translational Research Seminar, featuring a special talk by Neil Sarkar, PhD, director of the Brown Center for Biomedical Informatics and interim President and CEO of the Rhode Island Quality Institute. 

    Dr. Sarkar will provide a presentation on CurrentCare and other RIQI initiatives and resources that are available to RI investigators for use in their research. 

    Please join us in person, or register to watch the seminar via livestream

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Dec
    19
    12:00pm - 1:00pm

    NIA IMPACT Collaboratory Grand Rounds: Monica Taljaard, PhD

    121 South Main Street

    The National Institute on Aging (NIA) Imbedded Pragmatic AD/ADRD Clinical Trials (IMPACT) Collaboratory is working to build the nation’s capacity to conduct pragmatic clinical trials of interventions embedded within health care systems for people living with dementia and their caregivers. The IMPACT Collaboratory hosts free webinars on the 3rd Thursday of each month at 12 noon ET addressing these issues.

    On Thursday Dec. 19th we’ll hear from Monica Taljaard, PhD, a biostatistician specializing in the design, analysis and ethics of pragmatic cluster randomized and stepped wedge trials. As a member of the Ottawa Hospital Research Institute Methods Center, she regularly provides biostatistical assistance to investigators in the design, conduct, and analysis of pragmatic trials, pilot trials, quality improvement interventions, and health system projects. Dr. Taljaard will present “Stepped wedge cluster trials: what, how, and when?”

    Zoom Conferencing
    Join from PC, Mac, iOS or Android: https://hebrewseniorlife.zoom.us/j/5479652617
    Dial-In: +1 646 876 9923 (US Toll) or +1 669 900 6833 (US Toll)
    Meeting ID: 547 965 2617

    Biology, Medicine, Public Health, Research
  • Dec
    12
    2:00pm

    John M. K. Mislow, M.D., Ph.D. Memorial Lecture

    Sidney E. Frank Hall for Life Sciences

    Susan Hockfield, Ph.D.
    President Emerita
    Professor of Neuroscience
    Massachusetts Institute of Technology

     

    The Age of Living Machines: How Biology Will build the Next Technology Revolution

     

    Following the lecture, please join us for a reception in the Carney Institute Innovation Zone, 164 Angell St., fourth floor.

  • Dec
    11
    4:00pm

    Data Wednesday Seminar

    164 Angell Street

    PERFORMANCE AND IMPACT OF EHRs IN IMPROVING QUALITY OF CARE IN LOW AND MIDDLE INCOME COUNTRIES

    Hamish Fraser
    Associate Professor of Medical Science
    Brown Center for Biomedical Informatics

    Health information systems including Electronic Health Records are have been show to improve quality of care and reduce medical errors. In low and Middle income countries improving quality of care is a high priority especially for chronic diseases including HIV, MDR-TB, cardiovascular disease and Cancer. Electronic health records are increasingly widely used particularly to support HIV care. In this presentation I will discuss the current status of EHR deployments with the OpenMRS open source EHR, and the evidence for performance and clinical impact of OpenMRS in Rwanda and Kenya.

    Wednesday, December 11
    164 Angell Street, 3rd Floor Seminar Space
    4:00-5:00 PM

    Sponsored by DSI, CCMB, BCBI
    Refreshments will be served.

  • Ludovic Trinquart, PhD
    Dec
    9
    3:00pm - 4:00pm

    Statistics C. V. Starr Lecture - Ludovic Trinquart, PhD

    121 South Main Street

    Ludovic Trinquart PhD, Assistant Professor of Biostatistics, Boston University School of Public Health

    “Counterfactual mediation analysis with illness-death model for right-censored surrogate and clinical outcomes”

    We introduce a counterfactual-based mediation analysis for surrogate outcome evaluation when both the surrogate and clinical endpoint are subject to right-censoring. We use a multistate model for risk prediction to account for both direct transitions towards the clinical endpoint and transitions through the surrogate endpoint. We use the counterfactual framework to define natural direct and indirect effects and the proportion of the treatment effect on the clinical endpoint mediated by the surrogate endpoint. We define these quantities for the cumulative risk and restricted mean survival time. We illustrate our approach using 18-year follow-up data from the SPCG-4 randomized controlled trial of radical prostatectomy for prostate cancer. We assess time to metastasis as a potential surrogate outcome for all-cause mortality.

    Biology, Medicine, Public Health, BioStatsSeminar, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Training, Professional Development
  • Dec
    9
    8:00am - 1:00pm

    Advance-CTR Mentoring Training

    Petteruti Lounge

    Join Advance-CTR for the next installment of our highly rated Mentoring Training Program on December 9th and 16th at the Petteruti Lounge, 75 Waterman St, Providence.

    This is part one of the two part session. You must attend both session in order to received the completion certificate.

    Faculty who mentor junior investigators are encouraged to take advantage of this opportunity to grow as a research mentor and connect with colleagues.Participants will learn how to improve their relationships with mentees and become more effective mentors to junior investigators.

    This training will be facilitated by Diane Hoffman-Kim, PhD and Ulrike Mende, MD FAHA, who are trained by the National Research Mentoring Network to facilitate this nationally recognized mentoring curriculum.

    This peer-driven program expands mentors’ knowledge through exposure to the experiences of all participants. Attendees will engage with as many mentoring experiences as they would typically handle in a decade.

    Faculty who mentor junior investigators who conduct clinical and translational research are encouraged to apply. Preference will be given to more senior mentors.

    Learn more about the program on AdvanceCTR.org, or click the link above to register.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Please join the COBRE Center for Computational Biology of Human Disease, the Center for Computational Molecular Biology, and the Data Science Initiative for the COBRE CBHD Seminar.

    Shipra Vaishnava, Ph.D., is an Assistant Professor of Molecular Microbiology and Immunology at Brown University, and is one of the COBRE Center for Computational Biology of Human Disease’s success stories. She graduated from the COBRE program by receiving peer-reviewed funding.

    Dr. Vaishnava will present a talk entitled “Spatial Organization of Gut Bacterial Communities”.

    PLEASE NOTE: You will need to swipe your Brown ID before pressing the 3rd-floor button in the elevator to gain access to the third floor. Any Brown ID should work during business hours.

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Research
  • This course is designed to fulfill the NIH requirements for training in the Responsible Conduct of Research (RCR), and is coordinated by the Office of Graduate and Postdoctoral Studies (OGPS) in the Division of Biology and Medicine at Brown. The Research Integrity Series for Faculty consists of core and elective modules, with content and discussion topics aimed at more experienced scholars in the biomedical and clinical sciences.

    Requirements:
    Faculty must complete a minimum of 8 hours of in-person core and elective content in order to receive RCR certification. Faculty who began training in this course last year and have yet to complete their 8 hours may continue with this year’s series. Faculty registered for this course may apply up to 1 hr of in-person external RCR training (for example, a departmental workshop, class, or seminar relating to a topic covered in this class). Attendees must provide OGPS with verification of attendance for tracking purposes.

    Registration is required to attend. More information about the trainings will be distributed prior to the event. To register, please fill out this form.

    Schedule:

     

    Wednesday, December 4th, 3:30-5pm Dr. Elizabeth Harrington and Dr. Audra Van Wart, Rigor, Reproducibility, and Transparency (Note that this is one of the required trainings)

     

    SPRING Session (Dates are TBD but will include the following topics): Mentorship (2 hrs), Running a Lab, *Human Subjects/Animal Research, Data Management and Ownership

  •  

    Beth Bock, PhD

    Professor of Psychiatry & Human Behavior

    Centers for Behavioral and Preventive Medicine

    Brown Medical School and The Miriam Hospital

    Wednesday, December 4, 2019
    Butler Hospital ◊ Ray Hall Conference Center ◊ 11:00 am - 12:30 pm

    Objectives: At the conclusion of this presentation, participants should be able to:

    Describe the iterative process of intervention development; Explain the value of early process qualitative work; Discuss considerations for choosing technology platforms; and Explain the relative advantages of text messaging versus smartphone apps

    Disclosure: Beth Bock, PhD has no financial relationships to disclose.

    This activity is not supported by a commercial entity.

  • Dec
    3
    12:00pm - 1:00pm

    B-Lab & Brown Venture Prize Info Session (Dec.3)

    1 Euclid Avenue, Providence RI 02908

    Brown University’s Breakthrough Lab (B-Lab) is an intensive 8-week accelerator program designed to support student entrepreneurs developing high-impact ventures. We are agnostic to what type of venture we except into B-Lab and work with ventures from all backgrounds, from e-commerce to social impact. Each participating venture receives access to custom mentoring, a peer cohort of dedicated founders, co-working space, and a $4,000 summer stipend (per student).

    The Brown Venture Prize is designed to empower the most advanced entrepreneurial ventures by Brown students. It supports teams who have identified a significant opportunity, and whose ventures have the potential to create “impact at scale”. The prize is agnostic with respect to what sectors or industries ventures are working in, or even whether they are commercial, social, or have blended approaches. The essential thing is that teams have identified an opportunity or challenge and are thinking big about how to solve it. The Brown Venture Prize is intended to help them accelerate and scale those solutions. Winners will receive prize money, critical mentorship, and access to leaders in the Brown entrepreneurial community and beyond.

    Learn more about the two programs and ask questions about the application process. In addition to meeting students that participated in the programs, you will meet the Nelson Center team: Jason Harry, Director of Breakthrough Lab; Jonas Clark, Nelson Center, Associate Director; and Liz Malone, Assistant Director for Programs – who work with you during the whole process, coaching and cheering you on. If you just want to learn more or are thinking about applying to either program, join us!

    Lunch will be served. RSVP Here.

    Entrepreneurship, Graduate School, Postgraduate Education, Teaching & Learning
  • Dec
    2
    12:00pm - 5:00pm

    Math’s Bubbling (Not!) Over at ICERM

    121 South Main Street

    The National Museum of Mathematics founder Glen Whitney will be leading a participatory building activity at ICERM. Join us in our 11th-floor lecture hall at 121 S. Main Street, on Monday, Dec. 2. Please drop in anytime between 12 PM-5 PM to lend a hand and visit/chat with the participants of the Illustrating Mathematics program.

    In 1887, William Thomson, Lord Kelvin made a shrewd guess about the shape that numerous bubbles all the same volume would adopt when clustered together. He thought they would end up all identical, with six square facets and eight hexagonal ones. For slightly over a century, nobody was able to validate or refute Kelvin’s guess.

    Then in 1994 Irish physicists Denis Weaire and Robert Phelan discovered an elegant arrangement even more attractive to the bubbles, bursting Kelvin’s claim. Still, in the quarter-century since that time, nobody has been able to establish that this Weaire-Phelan foam, as it’s called, is truly the bubbles’ ideal state.

    Join the ICERM community of colleagues and friends to build a human-scale model of Weaire and Phelan’s surprising discovery about the nature of bubbles. Gain insight and appreciation for the surprising depth and beauty of everyday phenomena, that in this case led to a riddle that may stump and inspire problem-solvers for another 107 years.

  • Nov
    22
    3:30pm

    Data @ Brown: Poster Session

    164 Angell Street

    Join us in the DSI space to see some of the interesting data science research taking place in departments and units throughout Brown. Recipients of DSI grants and others will be sharing their projects. Light refreshments too!

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Physical & Earth Sciences, Psychology & Cognitive Sciences, Research, Social Sciences
  • Nov
    22
    1:00pm

    Community Wellness Informatics: Designing Technology for Health Equity

    Watson Center for Information Technology (CIT)

    Community Wellness Informatics: Designing Technology for Health Equity

    In the United States, there are serious and persistent disparities in health outcomes. For example, socioeconomic status is predictive of mortality and disease, with low-SES households disproportionately experiencing the poorest health outcomes. This inequality is due in large part to the social determinants of health—social, physical, and economic conditions that make it more challenging to achieve wellness in low-SES communities. Disruptive innovations are sorely needed to reduce health disparities. Information and communication technologies (ICTs), with their growing ubiquity and ability to provide engaging, informative, and empowering experiences for people, present exciting opportunities for health equity research. In this talk, I will present a set of case studies demonstrating work I’ve done to design, build, and evaluate how novel interactive computing experiences can address issues of health equity. These case studies investigate how social, mobile, and civic technology can help low-SES communities to both cope with barriers to wellness and address these barriers directly. Using findings from this research, I will articulate opportunities and challenges for community wellness informatics—research that explores how ICTs can empower collectives to collaboratively pursue health and wellness goals.

     

    Andrea Grimes Parker is an Assistant Professor at Northeastern University, with joint appointments in the Khoury College of Computer Sciences and the Bouvé College of Health Sciences. She is also a faculty scholar in Northeastern University’s Institute for Health Equity and Social Justice Research. She holds a Ph.D. in Human-Centered Computing from Georgia Tech and a B.S. in Computer Science from Northeastern University. Dr. Parker is the founder and director of the Wellness Technology Lab at Northeastern. Her interdisciplinary research spans the domains of human-computer interaction (HCI) and public health, as she examines how social and ubiquitous computing systems can be designed to promote wellness in vulnerable and marginalized populations. Dr. Parker’s research has been funded through grants from the National Science Foundation, the National Institutes of Health, and the Aetna Foundation. She has served on the program committees for the top conferences in HCI (e.g., ACM CHI, CSCW) and received several best paper nominations in these premier venues. From 2014-2016, Dr. Parker served as the National Evaluator for the Aetna Foundation’s portfolio of projects on mobile health interventions in community settings.

  • Nov
    22
    12:00pm

    DSCoV: DataLad

    164 Angell Street

    Data Science Computing and Visualization Workshop (DSCoV)

    Topic: DataLad
    Instructor: Yaroslav Halchenko


    DataLad provides a data portal and a versioning system for everyone, DataLad lets you have your data and control it too. For details, see https://www.datalad.org/.

    Friday, November 22 , 12:00 PM
    164 Angell Street, 4th Floor Innovation Space
    Organized by CCV; Sponsored by DSI

  • Nov
    21
    5:30pm - 7:30pm

    Closing Reception for Math+Art Exhibit

    Granoff Center for the Creative Arts

    Come celebrate the final evening of the successful Math+Art exhibit. The curated art on display is affiliated with ICERM’s fall “Illustrating Mathematics” semester-long research conference which brings together artists, makers, and mathematicians seeking to harness the creativity of mathematical illustrations to further the public’s understanding of mathematical research. 

    All are invited to enjoy light refreshments while mingling with ICERM’s visiting artists and mathematicians, renowned local artists, ”Gallery Night Providence” trolley participants, and members of the community.

    ICERM is grateful to the National Science Foundation and the Alfred P. Sloan Foundation award G-2019-11406 for making this event possible.

    Granoff Center at Brown University
    154 Angell Street, Providence, RI 02912
    Atrium Gallery

    Mathematics, Art, STEM, Research
  • Grant McKenzie
    Nov
    21
    3:00pm

    From Spatial to Platial Data Science

    164 Angell Street

    The availability of massive amounts of user-generated data has changed the way that spatial science research is conducted today as data synthesis and advanced computational equipment are now often a fundamental part of the scientific process. With the increased availability of these data, it is becoming apparent that the value of “big data” lies not necessarily in its size, but in its heterogeneity. As recent progress in data analytics and ambient intelligence is met with sensor-enabled mobile technology, geographic information science has pushed beyond “spatial” to incorporate non-explicitly geospatial contextual data. This heterogeneous “digital exhaust” has led us to develop computational, data-driven models of human behavior and take a multi-dimensional approach to investigating “place” and the
    activities people carry out at places. In this talk, Prof. Grant McKenzie will champion this move towards Platial data science through his research on reverse geocoding, language-based tourist attraction similarity, and shared micro-mobility services.

    Grant McKenzie is an assistant professor in the Department of Geography at McGill University in Montreal, Canada where he leads the Platial Analysis Lab, an interdisciplinary research group
    that works at the intersection of data science and behavioral geography.

    Co-sponsored by S4, PSTC, Data Science Initiative, Department of Sociology, and Department of Biostatistics & Center for Statistical Sciences. Part of the Re-Imagining Data Visualization Lectureship Series 2019-20 funded by the Herbert H. Goldberger Lectureship Fund.

  • This course is designed to fulfill the NIH requirements for training in the Responsible Conduct of Research (RCR), and is coordinated by the Office of Graduate and Postdoctoral Studies (OGPS) in the Division of Biology and Medicine at Brown. The Research Integrity Series for Faculty consists of core and elective modules, with content and discussion topics aimed at more experienced scholars in the biomedical and clinical sciences.

    Requirements:
    Faculty must complete a minimum of 8 hours of in-person core and elective content in order to receive RCR certification. Faculty who began training in this course last year and have yet to complete their 8 hours may continue with this year’s series. Faculty registered for this course may apply up to 1 hr of in-person external RCR training (for example, a departmental workshop, class, or seminar relating to a topic covered in this class). Attendees must provide OGPS with verification of attendance for tracking purposes.

    Registration is required to attend. More information about the trainings will be distributed prior to the event. To register, please fill out this form.

    Schedule:

     

    Wednesday, November 20th, 3:30 - 5pm Dr. Keri Godin, Research Misconduct and Brown University Policies (Note that this is one of the required trainings)

    Wednesday, December 4th, 3:30-5pm Dr. Elizabeth Harrington and Dr. Audra Van Wart, Rigor, Reproducibility, and Transparency (Note that this is one of the required trainings)

     

    SPRING Session (Dates are TBD but will include the following topics): Mentorship (2 hrs), Running a Lab, *Human Subjects/Animal Research, Data Management and Ownership

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Research, Training, Professional Development
  • Nov
    18

    The Computational Biology Core is hosting Qiagen who will present the Igenuity Pathway Analyses (IPA) Lunch & Learn Seminar on Monday, November 18, 2019, from 12 noon to 2 p.m. to be followed by a 1-hour Q&A Session. This will be held at 70 Ship Street, Room 107.

    Registration is required and lunch will be provided.

    Please register by emailing:  [email protected]

     

    During this seminar you will learn how to:

    • Upload data, run core analyses and view summary
    • Use IPA to build networks and generate hypotheses
    • Quickly generate a molecular profile of a disease, phenotype or function, accessing underlying literature and evidence of mechanistic relationships
    • Find master regulators and causal reasoning of effects in omics profiles

    New Functionality!

    Analysis Match automatically identifies curated IPA datasets with significant similarities and differences, enabling you to compare results, validate interpretation and better understand causal connections between diseases, genes, and networks of upstream regulators.

    Library of core analyses, generated from curated human and mouse disease and oncology datasets (from SRA, GEO, Array Express, TCGA and more).

    12:00 PM to 2:00 PM      IPA Lunch Seminar
      2:00 PM to 3:00 PM      Q & A Session

    Instructor: Eric Seiser, PhD, Field Application Scientist, Qiagen

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research
  • Nov
    15
    12:00pm

    CANCELED: DSCoV: HPC Fundamentals

    164 Angell Street

    Data Science Computing and Visualization Workshop (DSCoV)

    Want to be a software master, start a tech company, or succeed in research? Ready to get your hands dirty and learn the data science and programming skills needed to solve real-world data science problems? Come to a DSCoV workshop! Open to all members of the Brown community, these lunch-hour workshops are led by Brown faculty, staff, and students.

    THIS WEEK’S TOPIC: HPC Fundamentals
    INSTRUCTOR: Helen Kershaw

    Registration is necessary; limited to 40 participants.

    Friday, November 15, 12:00 PM
    164 Angell Street, 4th
    Floor Innovation Space.
    Organized by Center for Computation and Visualization
    Sponsored by the Data
    ScienceInitiative
    Pizza and soda will be served.

  • RENKU is an open data science platform, produced through a joint venture of EPFL and ETH Zurich, that fosters research reproducibility, reusability, and collaboration, along with secure, federated, and discoverable data access. RENKU supports versioning of data and code, and tracks which results were produced by whom and when. Users can retrieve history and data provenance, go back in time to every step of published science, and reuse previously built tools in an infrastructure agnostic environment. More information can be found at https://datascience.ch/renku/.

    In this “bring your own data” workshop, Christine Choirat will first describe RENKU’s motivation and capabilities, lead open discussion of community needs, and guide hands-on creation of workflows and pipelines using the platform. Participants should feel free to bring their own laptops and data pipeline problems, or just observe to learn more about the available software tools.

    Host: Jason Ritt

    Presenter: Christine Choirat
    Chief Innovation Officer, Swiss Data Science Center

    Bio: Dr. Christine Choirat is Chief Innovation Officer at the Swiss Data Science Center, a unique joint venture between EPFL and ETH Zurich. The Center’s mission is to accelerate the use of data science and machine learning techniques within academic disciplines of the ETH Domain, the Swiss academic community at large, and the industrial sector. Dr. Choirat received her PhD in Applied Mathematics from Paris Dauphine University. She joined the Swiss Data Science Center in 2019, from the Harvard T. H. Chan School of Public Health, where she was a Senior Research Scientist and an instructor in the Master of Science in Health Data Science. Her research interests are statistical software, reproducible workflows, and environmental policy and health policy.

  • RENKU is an open data science platform, produced through a joint venture of EPFL and ETH Zurich, that fosters research reproducibility, reusability, and collaboration, along with secure, federated, and discoverable data access. RENKU supports versioning of data and code, and tracks which results were produced by whom and when. Users can retrieve history and data provenance, go back in time to every step of published science, and reuse previously built tools in an infrastructure agnostic environment. More information can be found at https://datascience.ch/renku/.

    In this “bring your own data” workshop, Christine Choirat will first describe RENKU’s motivation and capabilities, lead open discussion of community needs, and guide hands-on creation of workflows and pipelines using the platform. Participants should feel free to bring their own laptops and data pipeline problems, or just observe to learn more about the available software tools.

    Host: Jason Ritt

    Presenter: Christine Choirat
    Chief Innovation Officer, Swiss Data Science Center

    Christine Choirat

    Bio: Dr. Christine Choirat is Chief Innovation Officer at the Swiss Data Science Center, a unique joint venture between EPFL and ETH Zurich. The Center’s mission is to accelerate the use of data science and machine learning techniques within academic disciplines of the ETH Domain, the Swiss academic community at large, and the industrial sector. Dr. Choirat received her PhD in Applied Mathematics from Paris Dauphine University. She joined the Swiss Data Science Center in 2019, from the Harvard T. H. Chan School of Public Health, where she was a Senior Research Scientist and an instructor in the Master of Science in Health Data Science. Her research interests are statistical software, reproducible workflows, and environmental policy and health policy.

  • Please join the COBRE Center for Computational Biology of Human Disease, the Center for Computational Molecular Biology, and the Data Science Initiative for the COBRE CBHD Seminar.

    Amanda Jamieson, Ph.D., is an Assistant Professor of Molecular Microbiology and Immunology at Brown University and is one of the COBRE Center for Computational Biology of Human Disease’s success stories. She graduated from the COBRE program by receiving peer-reviewed funding.

    PLEASE NOTE: You will need to swipe your Brown ID before pressing the 3rd-floor button in the elevator to gain access to the third floor. Any Brown ID should work during business hours.

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Research
  • Vertica
    Nov
    12
    12:00pm

    DSI Industry Insight: Vertica Systems

    164 Angell Street

    THE VERTICA ANALYTIC DATABASE: AN INNOVATIVE STORAGE ARCHITECTURE AND ITS EVOLUTION

    Ryan Roelke
    Senior Software Engineer, Vertica Systems

    Vertica is one of the leading database management systems for Big Data analytics. In Vertica’s early days, customers trying out Vertica would have their database inquiries run up to 1000 times faster compared to their current database system. Fifteen years later, competing database management systems are catching up in query performance, but Vertica’s recent adaptations to a world of cloud computing have kept it ahead in its ability to scale increasingly-demanding workloads. This talk will discuss how Vertica’s innovative storage layer was able to achieve such large performance improvements in the early days, and how Vertica has adapted its storage architecture to capitalize on the prevalence of cloud computing.

    Tuesday, November 12
    12:00 PM - 1:00 PM
    164 Angell Street, 3rd Floor
    Sponsored by the Data Science Initiative

  • Nov
    11
    6:00pm

    Math+Art Panel Discussion

    121 South Main Street

    Please join us at ICERM to enjoy this final in a series of five panel discussions on Math + Art. These discussions feature participants in ICERM’s Illustrating Mathematics Fall 2019 program, artists, and RISD faculty. The panels focus on the different ways in which artists and mathematicians approach mathematical concepts. We expect a dynamic conversation that will spark continued dialogue and future collaborations.

  • This workshop will focus on the theoretical insights developed via illustration, visualization, and computational experiment in dynamical systems and probability theory. Some topics from complex dynamics include: dynamical moduli spaces and their dynamically-defined subvarieties, degenerations of dynamical systems as one moves toward the boundary of moduli space, and the structure of algebraic data coming from a family of dynamical systems. In classical dynamical systems, some topics include: flows on hyperbolic spaces and Lorentz attractors, simple physical systems like billiards in two and three dimensional domains, and flows on moduli spaces. In probability theory, the workshop features: random walks and continuous time random processes like Brownian motion, SLE, and scaling limits of discrete systems.

    Mathematics, Technology, Engineering
  • Nov
    8
    12:00pm

    DSCoV: Intro to GPU and CUDA

    164 Angell Street

    Data Science Computing and Visualization Workshop (DSCoV)

    Want to be a software master, start a tech company, or succeed in research? Ready to get your hands dirty and learn the data science and programming skills needed to solve real-world data science problems? Come to a DSCoV workshop! Open to all members of the Brown community, these lunch-hour workshops are led by Brown faculty, staff, and students.

    THIS WEEK’S TOPIC: Intro to GPU and CUDA
    INSTRUCTOR: Khemraj Shukla

    Registration is necessary; limited to 40 participants.

    Friday, November 8, 12:00 PM
    164 Angell Street, 4th
    Floor Innovation Space.
    Organized by Center for Computation and Visualization
    Sponsored by the Data
    ScienceInitiative
    Pizza and soda will be served.

  • Nov
    8
    9:00am - 12:00pm

    CBC Fundamentals of R Workshop

    Watson Center for Information Technology (CIT)

    Please join the Computational Biology Core (CBC) as we host the Introduction to R Workshop. The CBC staff at Brown University will provide training and information on the basics of the R ecosystem. The goal is to ensure that participants have gained a basic, working knowledge of R.

     Please register at the link below.

    DATE:            Friday, November 8, 2019
    TIME:             9:00 a.m. to 12:00 p.m.
    LOCATION:   CIT SWIG Boardroom (Room 241)
                          115 Waterman Street, Providence, RI 02912

    BRING:          Laptop with wireless capabilities and plug.

    REGISTER:   https://forms.gle/Q8QexQYZMXnK3KtRA

     

    In this workshop you’ll learn:

    • Basics of R (objects, variables, data classes, vectors)
    • How to use and write R functions
    • How to import and export your data
    • How to install and load R and Bioconductor packages
    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Research
  • Nov
    7
    8:00am - 4:00pm

    Advance-CTR Open House

    233 Richmond Street

    Come by our Open House on Thursday, November 7 to see what Advance-CTR is offering investigators this fall.

    Activities include:

    • Complimentary, professional headshots
    • Exclusive Good Clinical Practice Live registration for the first 5 attendees (course eligibility applies)
    • Meet our Service Cores: Are you getting the most out of our Service Cores? See what resources, educational offerings, and expertise are available to help you in your research
      • Biomedical Informatics Core: 9-11:30 a.m. & 2-4 p.m.
      • Biostatistics and Research Design Core: 8:30-11 a.m. & 1-4 p.m.
      • Clinical Research Core: 1-4 p.m.
    • Plus, snacks, games and more!

    We look forward to seeing you there! 

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Research, Social Sciences, Training, Professional Development
  • S. Krishnan
    Nov
    6

    Deep Lens: A Multimedia Database System for Fast and Accurate Video Analytics

    Sanjay Krishnan, UChicago

     

    The increasing sophistication of computer vision algorithms enables new types of automated analysis visual data. The DeepLens project explores a future where the primary consumers of streaming video are not human viewers but analytics algorithms – accordingly revisiting storage, compression, and retrieval with this change in mind.

    This talk presents three projects: (dl-storage) a video storage engine optimized for algorithmic video retrieval, (dl-pipelines) a distributed stream processing engine optimized for visual payloads, (dl-mt) neural network training and evaluation in resource-constrained multi-tenant environments.

    In all three of these projects, a core theme is to take a classical database systems problem, such as data skipping or concurrency, and determine what changes under visual analytics workloads. This talk concludes by presenting applications of DeepLens to traffic analytics and robotic surgery, and performance results that demonstrate scaling to over 1000 hours of high definition video per computing node.

     

    Sanjay Krishnan is an Assistant Professor of Computer Science at the University of Chicago. His research studies the intersection of machine learning and computer systems. Sanjay completed his PhD and Master’s Degree at the University of California, Berkeley in Computer Science in 2018.

    Sanjay’s work has received numerous awards including the 2016 SIGMOD Best Demonstration award, the 2015 IEEE GHTC Best Paper award, and the Sage Scholar award.

     

  • Nov
    1
    3:00pm

    CCV-Con Day 5: Keynote, Panel, Reception

    164 Angell Street

    The Brown University Center for Computation and Visualization (CCV) is proud to announce its first annual conference! This will feature talks and workshops related to a broad range of topics in research computing, including: high-performance computing, machine learning, reproducible science, data visualization, bioinformatics, research infrastructure, and software engineering. The Conference will be from 3:00 to 5:00 pm each day at the Data Science Initiative on the 3rd floor of 164 Angell St. Attendees are welcome to register for any number of sessions - registration can be found at ccv.brown.edu/ccvcon. The first 50 registered attendees to arrive each day will receive a swag-of-the-day.

    Biology, Medicine, Public Health, Government, Public & International Affairs, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Physical & Earth Sciences, Psychology & Cognitive Sciences, Research, Social Sciences, Training, Professional Development
  • Nov
    1
    12:00pm

    DSCoV: Cloud Computing with Google

    164 Angell Street

    Data Science Computing and Visualization Workshop (DSCoV)

    Want to be a software master, start a tech company, or succeed in research? Ready to get your hands dirty and learn the data science and programming skills needed to solve real-world data science problems? Come to a DSCoV workshop! Open to all members of the Brown community, these lunch-hour workshops are led by Brown faculty, staff, and students.

    THIS WEEK’S TOPIC: Cloud computing with Google Cloud Platform, interacting with and using the Compute Engine
    INSTRUCTOR: Isabel Restrepo

    Registration is necessary; limited to 40 participants.

    Friday, November 1, 12:00 PM
    164 Angell Street, 4th
    Floor Innovation Space.
    Organized by Center for Computation and Visualization
    Sponsored by the Data
    ScienceInitiative
    Pizza and soda will be served.

  • Oct
    31

    The Brown University Center for Computation and Visualization (CCV) is proud to announce its first annual conference! This will feature talks and workshops related to a broad range of topics in research computing, including: high-performance computing, machine learning, reproducible science, data visualization, bioinformatics, research infrastructure, and software engineering. The Conference will be from 3:00 to 5:00 pm each day at the Data Science Initiative on the 3rd floor of 164 Angell St. Attendees are welcome to register for any number of sessions - registration can be found at ccv.brown.edu/ccvcon. The first 50 registered attendees to arrive each day will receive a swag-of-the-day.

    Biology, Medicine, Public Health, Government, Public & International Affairs, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Physical & Earth Sciences, Psychology & Cognitive Sciences, Research, Social Sciences, Training, Professional Development
  • Glen Whitney, StudioInfity.org
    Oct
    30
    6:00pm - 7:00pm

    CANCELED: ICERM Public Lecture: The nth Perspective

    Granoff Center for the Creative Arts

    Due to unforeseen circumstances, ICERM’s public lecture, “The nth Perspective” has been canceled. But the reception is still on!


    Come enjoy light refreshments and the opportunity to interact with the artists-in-residence affiliated with our Illustrating Mathematics semester program and featured in the “Math+Art” exhibit now on display in Granoff’s Atrium Gallery.

    Arts, Performance, Mathematics, Technology, Engineering
  • Oct
    30
    3:00pm

    CCV-Con Day 3: Data Science

    164 Angell Street

    The Brown University Center for Computation and Visualization (CCV) is proud to announce its first annual conference! This will feature talks and workshops related to a broad range of topics in research computing, including: high-performance computing, machine learning, reproducible science, data visualization, bioinformatics, research infrastructure, and software engineering. The Conference will be from 3:00 to 5:00 pm each day at the Data Science Initiative on the 3rd floor of 164 Angell St. Attendees are welcome to register for any number of sessions - registration can be found at ccv.brown.edu/ccvcon. The first 50 registered attendees to arrive each day will receive a swag-of-the-day.

    Biology, Medicine, Public Health, Government, Public & International Affairs, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Physical & Earth Sciences, Psychology & Cognitive Sciences, Research, Social Sciences, Training, Professional Development
  • Participants solve mentoring dilemmas and share strategies for success.
    Oct
    30
    12:00pm - 4:30pm

    Advance-CTR Mentoring Training

    121 South Main Street

    Join Advance-CTR for the next installment of our highly rated Mentoring Training Program on October 23 and 30 at the Brown University School of Public Health.

    This is part two of the two part session. You must attend both session in order to received the completion certificate. 

    Faculty who mentor junior investigators are encouraged to take advantage of this opportunity to grow as a research mentor and connect with colleagues.Participants will learn how to improve their relationships with mentees and become more effective mentors to junior investigators.

    This training will be facilitated by Suzanne Colby, PhD, and Michael Mello, MD, MPH, who are trained by the National Research Mentoring Network to facilitate this nationally recognized mentoring curriculum.

    This peer-driven program expands mentors’ knowledge through exposure to the experiences of all participants. Attendees will engage with as many mentoring experiences as they would typically handle in a decade.

    Faculty who mentor junior investigators who conduct clinical and translational research are encouraged to apply. Preference will be given to more senior mentors.

    Learn more about the program on AdvanceCTR.org, or click the link above to register.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • The Brown University Center for Computation and Visualization (CCV) is proud to announce its first annual conference! This will feature talks and workshops related to a broad range of topics in research computing, including: high-performance computing, machine learning, reproducible science, data visualization, bioinformatics, research infrastructure, and software engineering. The Conference will be from 3:00 to 5:00 pm each day at the Data Science Initiative on the 3rd floor of 164 Angell St. Attendees are welcome to register for any number of sessions - registration can be found at ccv.brown.edu/ccvcon. The first 50 registered attendees to arrive each day will receive a swag-of-the-day.

    Biology, Medicine, Public Health, Government, Public & International Affairs, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Physical & Earth Sciences, Psychology & Cognitive Sciences, Research, Social Sciences, Training, Professional Development
  • Oct
    28
    3:00pm

    CCV-Con Day 1: Reproducible Science

    164 Angell Street

    The Brown University Center for Computation and Visualization (CCV) is proud to announce its first annual conference! This will feature talks and workshops related to a broad range of topics in research computing, including: high-performance computing, machine learning, reproducible science, data visualization, bioinformatics, research infrastructure, and software engineering. The Conference will be from 3:00 to 5:00 pm each day at the Data Science Initiative on the 3rd floor of 164 Angell St. Attendees are welcome to register for any number of sessions - registration can be found at ccv.brown.edu/ccvcon. The first 50 registered attendees to arrive each day will receive a swag-of-the-day.

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Physical & Earth Sciences, Psychology & Cognitive Sciences, Research, Social Sciences, Training, Professional Development
  • Oct
    25
    1:00pm - 2:00pm

    Data Use Agreement Training

    Horace Mann House

    Data Use Agreement Training 

    Did you know that there are only two authorized signatory officials at Brown for Data Use Agreements?

    Do you know what to do if you’re presented with electronic terms & conditions for data use when accessing a data set electronically?

    Do you know how to self-classify data in accordance with CIS’ Risk Levels?

     

    Join the Office of Research Integrity to learn the answers to these questions and more! We’ll go over policies and procedures for receiving data from external sources and sending data outside of Brown, and CIS will talk about Data Risk Classifications.

    This training is open to anyone at Brown; light refreshments available.

    October 25, 2019

    Horace Mann building, Room 103

    47 George Street 

     

     

     

    ORI, OVPR, Training, Professional Development
  • Oct
    25
    12:00pm

    DSCoV: Deep Learning for $150

    164 Angell Street

    Data Science Computing and Visualization Workshop (DSCoV)

    Want to be a software master, start a tech company, or succeed in research? Ready to get your hands dirty and learn the data science and programming skills needed to solve real-world data science problems? Come to a DSCoV workshop! Open to all members of the Brown community, these lunch-hour workshops are led by Brown faculty, staff, and students.

    THIS WEEK’S TOPIC: Deep learning for $150?
    INSTRUCTOR: Lakshmi Govindarajan

    Computing infrastructure for running inference on deep neural networks is usually a bottleneck due to the associated costs, handling expertise. In this tutorial we will look at a solution coupling the Raspberry Pi and one of Intel’s tools, the Neural Compute Stick, to alleviate some of these concerns. Minimal exposure to Python, Unix, Tensorflow would be helpful.


    Registration is necessary; limited to 40 participants.

    Friday, October 25, 12:00 PM
    164 Angell Street, 4th
    Floor Innovation Space.
    Organized by Center for Computation and Visualization
    Sponsored by the Data
    ScienceInitiative
    Pizza and soda will be served.

  • H. Fraser
    Oct
    23

    Diagnostic Decision Support for Patients: Opportunities and Risks

    Diagnosis has always been a core part of medical care and recently it has become an important area for research as new studies highlight the frequency and importance of misdiagnosis. Along with this trend, is a rapid growth in diagnostic decision support systems. These have a long history in health informatics primarily designed for physician use, recent systems increasingly target patients. Often termed Symptom Checkers, these web based tools or mobile apps claim to assist in diagnosis and/or triage decisions. In this presentation, he will review the types of symptom checkers available, the potential benefits and risks for patients, and the surprising lack of evaluation studies and evidence of safety. He will also discuss his research in this area and upcoming studies particularly focused on heart disease and emergency care. 

     

    HAMISH FRASER, M.B.Ch.B., M.R.C.P., M.Sc., FACMI, IAHSI

    Associate Professor of Medical Science, Brown Center for Biomedical Informatics, Brown University

    Associate Professor of eHealth, Leeds Institute of Health Sciences, University of Leeds, United Kingdom

     

    Dr. Fraser trained in General Medicine, Cardiology, and Knowledge-Based systems at Edinburgh University. He completed a fellowship in clinical decision making at MIT with a focus on diagnostic decision support for heart disease. His work has also focused on developing medical informatics tools for some of the most challenging environments in low income countries. As Director of Informatics at the leading Healthcare NGO Partners in Health, he co-founded and co-leads OpenMRS, an open source EMR project. He was an Assistant Professor of Medicine at Harvard Medical School from 2006-2015.  

    His main academic focus is in the evaluation of medical information systems and understanding the impact of information and communications worldwide. Dr. Fraser also focuses on improvement of care for non-communicable diseases, particularly heart disease. His recent work at Brown University has focused on diagnostic decision support systems for patients with heart disease and emergency care.

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering
  • his course is designed to fulfill the NIH requirements for training in the Responsible Conduct of Research (RCR), and is coordinated by the Office of Graduate and Postdoctoral Studies (OGPS) in the Division of Biology and Medicine at Brown. The Research Integrity Series for Faculty consists of core and elective modules, with content and discussion topics aimed at more experienced scholars in the biomedical and clinical sciences.

    Requirements:
    Faculty must complete a minimum of 8 hours of in-person core and elective content in order to receive RCR certification. Faculty who began training in this course last year and have yet to complete their 8 hours may continue with this year’s series. Faculty registered for this course may apply up to 1 hr of in-person external RCR training (for example, a departmental workshop, class, or seminar relating to a topic covered in this class). Attendees must provide OGPS with verification of attendance for tracking purposes.

    Registration is required to attend. More information about the trainings will be distributed prior to the event. To register, please fill out this form.

    Schedule:

    Wednesday, October 23rd, 3:30 - 5pm Dr. Audra Van Wart, Responsible Authorship and Peer Review

    Wednesday, November 20th, 3:30 - 5pm Dr. Keri Godin, Research Misconduct and Brown University Policies (Note that this is one of the required trainings)

    Wednesday, December 4th, 3:30-5pm Dr. Elizabeth Harrington and Dr. Audra Van Wart, Rigor, Reproducibility, and Transparency (Note that this is one of the required trainings)

     

    SPRING Session (Dates are TBD but will include the following topics): Mentorship (2 hrs), Running a Lab, *Human Subjects/Animal Research, Data Management and Ownership

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Research, Training, Professional Development
  • Oct
    23
    1:30pm - 2:30pm

    Open Access Week: Special Presentation

    121 South Main Street

    Open Access and Scientific Publishing   The landscape of scientific publishing continues to change rapidly, particularly since the emergence of Open Access publishing models. While researchers enjoy a growing array of options to make their work available to other scientists and the general public, the proliferation of competing publishing models and venues can prove difficult to navigate.

    This informational session is intended to help both new and established researchers make educated decisions when it comes time to publish their scientific reports and/or data. We will seek to define the different publishing models (e.g., paywall/toll-access and open access), subtypes of open (e.g., green, bronze, gold), evolving copyright and licensing models (e.g., Creative Commons), and manuscript/data repositories. Finally, we will highlight a list of reliable resources containing information about potential venue(s) for various research outputs, including services available to students and faculty via the Brown University Library.

    We will be joined by Jason Gantenberg, MPH, PhD Candidate, School of Public Health; Erin Anthony, Biostatistics & Public Health Librarian, Rockefeller Library; and Andrew Creamer, Scientific Data Management Specialist, Rockefeller Library.

    Biology, Medicine, Public Health, Libraries, Research
  • Participants solve mentoring dilemmas and share strategies for success.
    Oct
    23
    12:00pm - 4:30pm

    Advance-CTR Mentoring Training

    121 South Main Street

    Join Advance-CTR for the next installment of our highly rated Mentoring Training Program on October 23 and 30 at the Brown University School of Public Health.

    This is part one of the two part sessions. Both sessions must be completed in order to receive the certification. 


    Faculty who mentor junior investigators are encouraged to take advantage of this opportunity to grow as a research mentor and connect with colleagues.Participants will learn how to improve their relationships with mentees and become more effective mentors to junior investigators.

    This training will be facilitated by Suzanne Colby, PhD, and Michael Mello, MD, MPH, who are trained by the National Research Mentoring Network to facilitate this nationally recognized mentoring curriculum.

    This peer-driven program expands mentors’ knowledge through exposure to the experiences of all participants. Attendees will engage with as many mentoring experiences as they would typically handle in a decade.

    Faculty who mentor junior investigators who conduct clinical and translational research are encouraged to apply. Preference will be given to more senior mentors.

    Learn more about the program on AdvanceCTR.org, or click the link above to register. 

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Jill Pipher, Vice President for Research; Elisha Benjamin Andrews Professor of Mathematics
    Seny Kamara, Associate Professor of Computer Science
    Modern, or public key, cryptography was invented in the late 1970s. It makes it possible to send credit card numbers over the internet to a vendor you’ve never met or talked to; it is the foundation for digital signatures and digital currencies. At the beginning, governments tried to suppress information about cryptographic algorithms. Now, with mass encryption of data in phones and texting apps, the tension between government law enforcement and cryptography is rising again. We will have an informal and interactive discussion about some of the history, the current political and social issues and what lies ahead in cryptography.

  • Oct
    18
    12:00pm

    DSCoV: PyTorch Tutorial

    164 Angell Street

    Data Science Computing and Visualization Workshop (DSCoV)

    Want to be a software master, start a tech company, or succeed in research? Ready to get your hands dirty and learn the data science and programming skills needed to solve real-world data science problems? Come to a DSCoV workshop! Open to all members of the Brown community, these lunch-hour workshops are led by Brown faculty, staff, and students.

    THIS WEEK’S TOPIC: PyTorch Tutorial
    INSTRUCTOR: Minju Jung

    Registration is necessary; limited to 40 participants.

    Friday, October 18, 12:00 PM
    164 Angell Street, 4th
    Floor Innovation Space.
    Organized by Center for Computation and Visualization
    Sponsored by the Data
    ScienceInitiative
    Pizza and soda will be served.

  • The Colloquium Series at the Population Studies and Training Center will host Mario Small, Professor of Sociology at Harvard University. His talk is titled “When Does Neighborhood Poverty Matter? Using Large-Scale Data to Understand Heterogeneity” and will examine how “big” data has furthered the understanding of neighborhood poverty in the context of race and class.

    Link: https://www.brown.edu/academics/population-studies/events

  • Stoyanovich
    Oct
    16

    FOLLOW THE DATA! RESPONSIBLE DATA SCIENCE STARTS WITH RESPONSIBLE DATA MANAGEMENT

    Data science technology promises to improve people’s lives, accelerate scientific discovery and innovation, and bring about positive societal change. Yet if not used responsibly, this same technology can reinforce inequity, limit accountability, and infringe on the privacy of individuals.

    In this talk, Julia will discuss recent technical work in the scope of the “Data, Responsibly” project and connect technical insights on fairness, diversity, transparency, and data protection to ongoing regulatory efforts in the United States and elsewhere.

    JULIA STOYANOVICH

    Assistant Professor
    Dept. of Computer Science and Engineering Tandon School of Engineering
    Center for Data Science
    New York University

    Julia Stoyanovich is an Assistant Professor at New York University in the Department of Computer Science and Engineering at the Tandon School of Engineering, and the Center for Data Science. Julia’s research focuses on responsible data management and analysis practices: on operationalizing fairness, diversity, transparency, and data protection in all stages of the data acquisition and processing lifecycle. She established the Data, Responsibly consortium and serves on the New York City Automated Decision Systems Task Force,by appointment from Mayor de Blasio. In Spring 2019, Julia developed and taught a course on Responsible Data Science at NYU. In addtition to data ethics, Julia works on management and analysis of preference data, and on querying large evolving graphs. She holds a Master of Science and PhD degrees in Computer Science from Columbia University and a Bachelor of Science in Computer Science and in Math and Statistics from the University of Massachusetts, Amherst. Julia’s work has been funded by the NSF, BSF, and by industry. She is a recipient of an NSF Career Award and of an NSF/CRA CI Fellowship.

  • We are excited to announce a joint seminar between the Emergency Digital Health Innovation program (EDHI) and the Brown Center for Biomedical Informatics (BCBI) featuring Ada Health Inc.
    Please join us on Tuesday, October 15 at 3:00 - 4:00p for a presentation on “The challenges of obtaining and using high quality data sets for developing and testing diagnostic algorithms - focus on Symptom Assessment apps and Diagnostic Decision Support Systems”
     
    Dr. Stephen Gilbert, PhD, MRes, BSc is the Clinical Evaluation Director at Ada Health. Dr. Gilbert is a clinical studies specialist and physiologist, and veterinary surgeon. He has over 10 years of experience in human physiology and trials including validating and clinically evaluating medical diagnostic and therapeutic devices, software, and drugs. He has worked for 13 years as a computational physiologist in research groups developing mathematical physiology and cell biology approaches. He has extensively published peer-reviewed research in these areas.
    Dr. Vishaal Virani is the Business Development & Client Successes Director at Ada Health. Dr. Virani is a physician by background and has been with Ada for nearly three years. He is focused on client partnerships and clinical evaluation at Ada, leading on the partnerships Ada has with Sutter Health and the NHS, among others.
    *Location: 55 Claverick Street, room 102
    *Time: Tuesday, October 15, 2019 @ 3:00-4:00pm
    ***RSVP here, and we will send you the calendar invite***
    Contact us at [email protected], [email protected], or [email protected] with any questions. We hope to see you there!
  • Oct
    11

    INVESTIGATING SPORTS BIAS WITHIN A LARGE CORPUS OF AMERICAN FOOTBALL BROADCASTS

     

    MOHIT IYYER, PhD

    Assistant Professor, Computer Science

    University of Massachusetts, Amherst

     

    Sports broadcasters inject drama into play-by-play commentary by building team and player narratives through subjective analyses and anecdotes. Prior studies based on small datasets and manual coding show that such theatrics evince commentator bias in sports broadcasts. To examine this phenomenon, we assemble FOOTBALL, which contains 1,455 broadcast transcripts from American football games across six decades that are automatically annotated with 250,000 player mentions and linked with metadata. We identify major confounding factors for researchers examining racial bias in FOOTBALL, and perform a computational analysis that supports conclusions from prior social science studies. 

     

    Mohit Iyyer is an assistant professor in computer science at UMass, Amherst. Previously, he was a Young Investigator at AI2. He completed his PhD at the University of Maryland, College Park, advised by Jordan Boyd-Graber and Hal Daumé III. His research interests lie broadly in natural language processing and machine learning. Much of his work uses deep learning to model language at the discourse level. Problems in this vein that he is currently excited about include efficiently generating coherent text, answering questions about documents, and understanding narratives in fictional text. 

     

    Register to meet with Mohit here: https://bit.ly/2AReXx5

  • Oct
    11
    12:00pm

    DSCoV: nGraph Tutorial

    164 Angell Street

    Data Science Computing and Visualization Workshop (DSCoV)

    Want to be a software master, start a tech company, or succeed in research? Ready to get your hands dirty and learn the data science and programming skills needed to solve real-world data science problems? Come to a DSCoV workshop! Open to all members of the Brown community, these lunch-hour workshops are led by Brown faculty, staff, and students.

    THIS WEEK’S TOPIC: nGraph Tutorial
    INSTRUCTOR: Hanlin Tang

    Registration is necessary; limited to 40 participants.

    Friday, October 11, 12:00 PM
    164 Angell Street, 4th
    Floor Innovation Space.
    Organized by Center for Computation and Visualization
    Sponsored by the Data
    ScienceInitiative
    Pizza and soda will be served.

  • Oct
    10
    12:00pm - 1:00pm

    Colloquium: Towards Automated Operation of Computing Systems (Ayse Coskun, Boston University)

    Watson Center for Information Technology (CIT)

    Today’s large-scale computers, such as high-performance computing clusters or the cloud, experience growing challenges in delivering predictable performance—and also, in maintaining efficiency, resilience, and security. Much of computer system management has traditionally relied on expert analysis and manual hands-on diagnostics. In this talk, I will demonstrate my group’s recent work on designing “automated analytics” methods for computing systems, leading the path towards a longer term vision where complex computing systems are able to self-manage and improve. Specifically, I will first talk about how to systematically diagnose root causes of performance variations (or “anomalies”) on large-scale computers, which cause substantial efficiency losses and higher cost in today’s supercomputers. Second, I will introduce machine learning-based methods to discover applications on HPC or cloud systems and discuss how such discoveries can help reduce vulnerabilities and avoid unwanted applications. This talk will also highlight methods for meaningful data collection from computing systems, demonstrate tools to help standardize study of performance anomalies, and point out future directions in automating computing system management.

    Prof. Ayse K. Coskun is currently an associate professor at Boston University (BU), Electrical and Computer Engineering Department. Her research interests include design automation, architecture, and systems, with a particular focus on energy efficiency and intelligent analytics for computing systems, spanning from small-scale mobile devices and emerging chip technologies to large-scale computers. She received her PhD degree from University of California San Diego (UCSD), Computer Science and Engineering Department. She worked at Sun Microsystems (now Oracle) prior to her appointment at BU. Prof. Coskun is currently an associate editor of the IEEE Transactions on Computers and serves in the executive committee of the IEEE Council on EDA (CEDA). She received the NSF CAREER award (2012), several best paper awards, and the IEEE CEDA Ernest Kuh Early Career Award (2017). Coskun was recently selected to attend the National Academy of Engineering’s Frontiers of Engineering Symposium in 2019.

    Host: Professor Iris Bahar

  • Oct
    10
    12:00pm - 1:00pm

    Translational Research Seminar Series

    Providence VA Medical Center

    This month’s seminar features: 

    • Amin Zand Vakili, MD, PhD: “Mapping PTSD Symptoms to Brain Networks: a Machine Learning Study”
    • Nishant Shah, MD: “PROPER-Stress: A Novel Protocoling Workflow to Improve the Appropriateness, Timeliness and Downstream Impact of Cardiac Stress Tests”

    Can’t make it in person? Watch the seminar remotely.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Research, Training, Professional Development
  • Applied Mathematics Colloquium

    Thursday, October 10, 2019
    12:00pm
    Room 108, 170 Hope Street

    Constantinos Daskalakis, MIT

    Title: Statistical Inference from Dependent Data

    Abstract: Statistical Learning Theory has largely focused on learning and generalization from independent and identically distributed observations. This assumption is, however, too strong. In many applications, observations are collected on nodes of a network, or some spatial or temporal domain, and are dependent. Examples abound in financial and meteorological applications, and dependencies naturally arise in social networks through peer effects. We study the basic statistical tasks of linear and logistic regression on networked data, where the responses on the nodes of the network are not independent conditioning on the nodes’ vectors of covariates. Given a single observation (across the whole network) from a networked linear or logistic regression model and under necessary weak dependency assumptions, we prove strong consistency results for estimating the model parameters, recovering the rates achievable in the standard setting with independent data. We generalize these results beyond linear and logistic regression, assuming that the observations satisfy Dobrushin’s condition, showing how to use Gaussian complexity and VC dimension to control generalization error.

  • Oct
    10
    8:30am - 12:00pm

    REDCap Workshop: Creating Databases

    70 Ship Street

    This class offers a comprehensive look at creating a database and using surveys. This longer session will cover topics from the getting started workshop and the surveys workshop. Time at the end of class will be dedicated to individual questions.

    Requisites: Attendees must watch 3 introductory videos before class that will be emailed to you after you register.

    Registration is required as space is limited. 

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Please join the COBRE Center for Computational Biology of Human Disease, the Center for Computational Molecular Biology, and the Data Science Initiative for the COBRE CBHD Seminar.

    Ashok Ragavendran will present on the available tools and pipelines of the Computational Biology Core.

     

    PLEASE NOTE: You will need to swipe your Brown ID before pressing the 3rd-floor button in the elevator to gain access to the third floor. Any Brown ID should work during business hours.

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Research
  • Many people fear automation. They may see it as a potential job killer. They may also be concerned about what can be automated. Could we train a computer to amplify human ability? Should we?

    Our ability to be creative, use social learning, and imitation set us apart from other species. In this talk, I will provide some examples of how we can use those social theories to guide algorithms to 1) identify interpretable behavioral patterns; 2) confirm, elaborate and design interventions to better understand how the real-world works; 3) deploy the solutions to measure impact. By carefully designing appropriate applications of Human-Centered AI, we show that technology can improve important social and cognitive skills including the lives of disadvantaged, ill, disabled and other individuals who struggle with socio-emotional communication, such as those with autism, severe anxiety, neurodegenerative disease, and terminal illness.

    In this talk, I will offer insights gained from our exploration of several questions: How are humans able to improve important social and cognitive skills with an intelligent system? What aspect of the feedback helps the most? How to deploy such systems to promote equality and access to health care and education.

    M. Ehsan Hoque is an assistant professor of computer science and an Asaro-Biggar (’92) Family Fellow in Data Science at the University of Rochester, where he leads the Rochester Human-Computer Interaction (ROC HCI) Group. From 2018-2019, he was also the Interim Director of the Georgen Institute for Data Science. Ehsan earned his Ph.D. from the MIT in 2013, where the MIT Museum highlighted his dissertation of developing and intelligent agent to improve human ability as one of MIT’s most unconventional inventions.

    Ehsan and his group’s work have been recognized through NSF CRII, NSF CAREER, and PECASE (to be announced in 2020) as well as MIT TR35, a commendation in Science News as one of ten early- to mid-career scientists to watch in 2017. In 2018, in collaboration with colleagues from the University of Rochester Medical Center (URMC), he has helped establish Morris K Udall Center for Parkinson’s Disease Research Center of Excellence through a $9.2M NIH grant. Ehsan is an associate editor of IEEE Transactions on Affective Computing(2015-2019), PACM IMWUT (2016-current), and Digital Biomarkers (2018-current). He is an inaugural member of the ACM’s Future of Computing Academy.

    Follow the ROC HCI group’s work on Twitter at @rochci

    Host: Professor Jeff Huang

  • Oct
    9
    12:00pm - 1:00pm

    Science and Security

    Horace Mann House

    To ensure the widest possible dissemination of research results, universities focus most of their efforts on conducting basic research that is intended to be shared and published openly in the scientific literature. There are, however, instances when information and technology generated in universities cannot be shared widely for a variety of reasons, including national and economic security. In these instances, universities and its researchers must strike a balance between scientific openness and controlling the release of information.

    In this session, we will address several topics that academic researchers should have basic knowledge of: export controls and deemed exports; classified vs. fundamental research; data sharing and protection; and sharing of research materials. We will also discuss the increased scrutiny and legislative action from the U.S. government in response to a perceived increase of academic espionage and theft of intellectual property.

    Education, Teaching, Instruction, ORI, OVPR, Training, Professional Development
  • Schlumberger: Deep learning lightning technical talk and career lunch

    Tuesday, October 8,  12pm - 12:50pm

    164 Angell Street, 3rd Floor Seminar Space (Entrance on Angell Street, behind Brown Bookstore). 

    Schlumberger-Doll Research is visiting to share some extraordinary and fascinating Data Science research and to recruit for Data Science roles. Full time and internship opportunities are available at all levels (including Masters and PhDs). Ideal candidates will know about data science, and also have some additional engineering and or scientific training. (Students interested in engineering, physics, applied math, statistics, math, and computer science students are encouraged to attend).

    Please reserve your place using this form. Smaller group information sessions for interested participants will also be held at 4 pm.

    The team will present a few short technical talks highlighting the research directions and projects currently being conducted in the applied math and data analytics department. Research activities will be presented on topics related to subsurface data science, including deep learning for automation of interpretation algorithms, uncertainty quantification within the deep learning realm, and Bayesian causal networks for modeling and reasoning. 

    They will also provide a brief overview of the company, its research centers, and employment and internship opportunities.

    Speakers:

    Dr. Smaine Zeroug, Research Director, Applied Math and Data Analytics
    Dr. Lalitha Venkataramanan, Research Program Manager
    Dr. Marie LeFranc, Research Program Manager
    Dr. Peter Tilke, Scientific Advisor

    Open Positions: Descriptions of currently open Data Scientist and Intern positions with Schlumberger-Doll are located here. If you are interested in applying, please be sure to follow the yellow highlighted instructions. There are many other opportunities at Schlumberger.

    Information Sessions: At 4:00, the team will host small information sessions to answer questions related to the content presented and internships and employment with Schlumberger. An information session sign-up sheet link will be provided during the presentation.

    About Schlumberger: Schlumberger is the world’s largest oilfield services company and employs approximately 100,000 people representing more than 140 nationalities working in more than 85 countries. It specializes in helping companies to characterize their oil and gas resources by locating them precisely and then modeling all aspects of their extraction. A constant theme in the companies history has been cutting edge research.

    Questions? For logistical questions, please email [email protected]; For industry-related questions, please email [email protected].

  • Oct
    7
    4:00pm

    Math+Art Panel Discussion

    121 South Main Street

    Please join us at ICERM to enjoy this third in a series of five panel discussions on Math + Art. These discussions feature participants in ICERM’s Illustrating Mathematics Fall 2019 program, artists, and RISD faculty. The panels focus on the different ways in which artists and mathematicians approach mathematical concepts. We expect a dynamic conversation that will spark continued dialogue and future collaborations.

  • Fan Li, PhD
    Oct
    7
    3:00pm - 4:00pm

    Statistics Seminar, Fan Li, PhD

    121 South Main Street

    Fan Li, PhD, Associate Professor, Department of Statistical Science, Duke University

    “Introducing the Overlap Weights in Causal Inference”

    Covariate balance is crucial for confounding adjustment in causal studies with observational data. We propose a unified framework —the balancing weights– to balance the weighted distributions of the covariates between treatment groups. These weights incorporate the propensity score to weight each group to an analyst-selected target population, and include several commonly used weighting schemes such as inverse-probability weight and trimming as special cases. We derive the large-sample results on nonparametric estimation based on these weights. We further propose a new weighting scheme, the overlap weights, in which each unit’s weight is proportional to the probability of that unit being assigned to the opposite group. The overlap weights are bounded, and minimize the asymptotic variance of the weighted average treatment effect among the class of balancing weights. We prove a small-sample exact balance property of the overlap weights. We apply the method the Framingham Heart Study to evaluate the effect of statins on health outcomes. Extension to multiple treatments will also be discussed.

    Biology, Medicine, Public Health, BioStatsSeminar, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Training, Professional Development
  • Oct
    4
    12:00pm

    DSCoV Workshop: Git Introduction

    164 Angell Street

    Data Science Computing and Visualization Workshop (DSCoV)

    Want to be a software master, start a tech company, or succeed in research? Ready to get your hands dirty and learn the data science and programming skills needed to solve real-world data science problems? Come to a DSCoV workshop! Open to all members of the Brown community, these lunch-hour workshops are led by Brown faculty, staff, and students.

    THIS WEEK’S TOPIC: Getting started with Git and version control best practices.
    INSTRUCTOR: Singh Saluja

    Registration is necessary; limited to 40 participants.

    Friday, October 4, 12:00 PM
    164 Angell Street, 4th
    Floor Innovation Space.
    Organized by Center for Computation and Visualization
    Sponsored by the Data
    ScienceInitiative
    Pizza and soda will be served.

  • Oct
    4
    12:00pm

    i-BSHS Seminar Series - Belinda Borrelli, PhD

    121 South Main Street
    Please join us for the i-BSHS Seminar Series kick-off lecture, presented by Belinda Borrelli, PhD.
     
    “Stealth Interventions to Motivate and Sustain Health Behavior Change: High Tech and High Touch Approaches”
    Flyer for Belinda Borrelli
    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research, Social Sciences, Training, Professional Development
  • Oct
    4
    9:00am - 12:00pm

    CBC Workshop: Visualization in R with tidyverse and ggplot2

    Watson Center for Information Technology (CIT)

    Extending the series of the Introduction to R workshops, the Computational Biology Core will be running a workshop specifically on creating publication-quality graphs and exploratory visualizations in R under the framework of tidyverse and ggplot2, based on materials in R for data science.

    This workshop will be held on Friday, October 4, 2019, in the CIT SWIG Board Room from 9 AM to to 12 PM. We ask that participants have either gone through our Fundamentals of R workshop or have other prior familiarity with R.

    Please register here if you would like to participate (requires a Brown-affiliated account):  https://forms.gle/SUzcEAMNvduDHRTL7

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Research
  • Oct
    3
    9:00am - 10:00am

    Engineering Seminar: Biomedical Engineering

    Barus and Holley

    Ryan McGinnis, Assistant Professor in the Department of Electrical and Biomedical Engineering at the University of Vermont, will present a talk: “Wearables and the Digital Health Revolution”.

    Abstract: Wearables are at the heart of the digital health revolution. A core aspect of digital health is the promise of improved patient monitoring, allowing assessment and personalized intervention to occur in near real time. However, the data gathered from current wearables are often too general (e.g. gross measures of physical activity) and do not provide the biomechanical or physiological insight necessary to deliver on this vision. In this talk, I will provide background on the emerging field of digital health and describe the first steps we’ve taken toward its promise by presenting novel assessments for identifying mental health problems in young children and a new framework that automates wearable sensor-based remote patient monitoring for those with neurological disease and recovering from orthopedic surgery.

  • Dhananjay Bhaskar
    Oct
    2
    4:00pm

    Data Wednesdays: Dhananjay Bhaskar

    164 Angell Street

    INVESTIGATING TUMOR INVASION USING MACHINE LEARNING AND TOPOLOGICAL DATA ANALYSIS

    Our group focuses on epithelial-to-mesenchymal transition, or EMT, a key driving force in tissue invasion. In this talk, Dhananjay will describe the group’s methodology for analyzing time-lapse microscopy images to quantify EMT and to develop agent-based models of cellular motility. He’ll then outline the challenges faced when applying this technique to 3D confocal acquisitions and propose a novel method using topological data analysis, or TOA, to overcome these challenges.

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Research
  • Academic Grand Rounds*

    Realizing the societal benefits of medical research: Applying implementation science to improve clinical practice and patient outcomes

    Brian S. Mittman, PhD

    Senior Scientist, Health Services Research and Implementation Science

    Kaiser Permanente Southern California Department of Research and Evaluation

    Co-Lead, Implementation and Improvement Science Initiative

    UCLA Clinical and Translational Science Institute

    Wednesday, October 2, 2019
    Butler Hospital ◊ Ray Hall Conference Center ◊ 11:00 am - 12:30 pm

    Objectives: At the conclusion of this presentation, participants should be able to: Articulate the definition, goals and scope of implementation science and explain how it relates to other categories of health-related research; Identify key phases of pre-implementation and implementation studies and explain key differences in the aims and purpose of each phase; and to Select appropriate research designs and methods for different types and phases of implementation studies.

    Disclosure: Brian S. Mittman, PhD has no financial relationships to disclose.

    This activity is not supported by a commercial entity.

  • Sep
    30
    3:30pm - 5:00pm

    Research Integrity Series: The Role of the Scientist in Society

    Brown University Medical Education Building (Alpert Medical School)

    This course is designed to fulfill the NIH requirements for training in the Responsible Conduct of Research (RCR), and is coordinated by the Office of Graduate and Postdoctoral Studies (OGPS) in the Division of Biology and Medicine at Brown. The Research Integrity Series for Faculty consists of core and elective modules, with content and discussion topics aimed at more experienced scholars in the biomedical and clinical sciences.

    Requirements: 
    Faculty must complete a minimum of 8 hours of in-person core and elective content in order to receive RCR certification. Faculty who began training in this course last year and have yet to complete their 8 hours may continue with this year’s series. Faculty registered for this course may apply up to 1 hr of in-person external RCR training (for example, a departmental workshop, class, or seminar relating to a topic covered in this class). Attendees must provide OGPS with verification of attendance for tracking purposes.

    Registration is required to attend. More information about the trainings will be distributed prior to the event. To register, please fill out this form

    Schedule: 

    Monday, September 30th 3:30 - 5pm Dr. Jim Padbury and Dr. Ed Hawrot, The Role of the Scientist in Society

    Wednesday, October 23rd, 3:30 - 5pm Dr. Audra Van Wart, Responsible Authorship and Peer Review

    Wednesday, November 20th, 3:30 - 5pm Dr. Keri Godin, Research Misconduct and Brown University Policies (Note that this is one of the required trainings)

    Wednesday, December 4th, 3:30-5pm Dr. Elizabeth Harrington and Dr. Audra Van Wart, Rigor, Reproducibility, and Transparency (Note that this is one of the required trainings)

     

    SPRING Session (Dates are TBD but will include the following topics): Mentorship (2 hrs), Running a Lab, *Human Subjects/Animal Research, Data Management and Ownership

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Research, Training, Professional Development