Past Events

  • Sep
    21
    4:00pm - 5:00pm

    DSI Colloquium: Professor Michael Littman

    Watson Center for Information Technology (CIT)

    MICHAEL LITTMAN
    PROFESSOR OF COMPUTER SCIENCE, BROWN UNIVERSITY


    ASSESSING AND IMPROVING GENERALIZATION IN DEEP REINFORCEMENT LEARNING

    Deep reinforcement-learning approaches have been shown to produce a remarkable performance on a range of challenging control tasks. Observations of the resulting behavior give the impression that agents construct rich task representations that support insightful action decisions. Looking closely at the generalization capacity of these deep Q-networks, however, we find that the learned value computations often reduce to brittle memorization and that the network does not know how to handle even small non-adversarial modifications to the states it encounters during execution. We examine training methods that improve generalization capability. Our results provide strong evidence that not all deep networks learn robust behaviors, and that careful consideration must be made to achieve results to the contrary. 

     

    Coffee and pastry to be served.

    Academic Calendar, University Dates & Events, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research
  • Sep
    26
    8:00am - 4:00pm

    Advance-CTR Open House

    233 Richmond Street

    Come out for food, fun, and free headshots at our Open House on Wednesday, September 26.

    The Advance-CTR team welcomes investigators and research staff to this all-day event (drop by any time) at our headquarters in Providence, RI.

    Activities include:

    • Complimentary, professional headshots
    • Exclusive sign-up for REDCap workshops
    • “Ask a Biostatistician:” Informal consults with our experts about your proposal ideas or research questions
    • Ask us anything: Pilot Projects application process, Mentored Research Awards, and Research Services
    • Prizes, games, and more!

    Wednesday, September 26 from 8 a.m. - 4 p.m. at 233 Richmond Street, Suite 200, Providence, RI (across from the Alpert Medical School).

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Nicolo Fusi, PhD

    Head of Automated Machine Learning Research

    Microsoft Research

    Cambridge, MA

    “From Gene Editing to Automated Machine Learning: Using AI to Tune Complex Systems”

    Complex systems often need careful tuning in order to obtain optimal performance and robustness. For example, while the CRISPR/Cas9 system provides state-of-the art genome editing capabilities, several facets of this system are under investigation for further characterization and optimization. One in particular is the choice of guide RNA that directs Cas9 to target DNA–given that one would like to target the protein-coding region of a gene, hundreds of guides satisfy the constraints of the CRISPR/Cas9 Protospacer Adjacent Motif sequence. However, only some of these guides efficiently target DNA to generate gene knockouts (i.e. have high on-target efficiency) and some of these might have unintended (i.e. off-target) effects somewhere else in the genome. In this talk, I present two state-of-the-art machine learning approaches to predict both on-target efficiency and the presence of off-target effects (both accessible at http://crispr.ml ). I then discuss how machine learning pipelines can themselves be optimized using machine learning. Finally, I show that our approach to tune machine learning pipelines can be more generally used to tune parameters of any system (not just machine learning ones) by exploiting information gathered from multiple related experiments.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Oct
    3
    4:00pm - 5:00pm

    DSI Colloquium: Joseph Cappelleri

    Watson Center for Information Technology (CIT)

    More information to be posted. 

    Academic Calendar, University Dates & Events, Biology, Medicine, Public Health, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research
  • Oct
    8

    Indigenous Peoples Day Holiday

    University Services & Operations
  • Oct
    10
    4:00pm

    Statistical and Geometric Methods for the Analysis of Sequence Count Data

    Watson Center for Information Technology (CIT)

    Justin Silverman
    Computational Biology & Bioinformatics
    Duke University

    Statistical and Geometric Methods for the Analysis of Sequence Count Data
    Sequence counting via high-throughput DNA sequencing underlies many studies including 16s rRNA sequencing as well as single-cell or bulk RNA sequencing. However, due to the the measurement process, sequence count data contains information regarding only the relative abundances of sequences. Such relative data, often termed compositional, is known to cause problems in many statistical analyses. Moreover other forms of technical variation and bias complicate analysis of sequence count data. In this talk I will introduce statistical and geometric tools to address these challenges. In particular, by combining these methods with experimental design, I will demonstrate how biological and technical variation in longitudinal studies can be partitioned. Additionally, I will discuss new models that both enable quantification of the sensitivity of real data to compositional effects while also allowing the reconstruction of differential expression and correlation analysis by model augmentation.
    Hosted by Lorin Crawford
  • Oct
    11
    12:00pm - 1:00pm

    Advance-CTR Seminar Series

    Rhode Island Quality Institute

    This month’s Advance-CTR Seminar Series features two presentations on research using big data. The presenters are: 

    • Shira Dunsiger, PhD, and Beth Bock, PhD:  “Exploring Second-to-Second Exercise Intensity and Disease Risk Outcomes;” and 
    • Thomas Serre, PhD:  “Automating Pathology with Deep Learning” 

    Can’t make it in person? Register to watch the Seminar remotely

    Find more directions and parking directions online at AdvanceCTR.org

    Biology, Medicine, Public Health, Research
  • Robert Heckendorn
    Oct
    17
    4:00pm

    The Hitchhiker’s Guide to Epistasis: the Galactic View

    Watson Center for Information Technology (CIT)

    Dr. Robert Heckendorn
    Dept. of Computer Science
    University of Idaho

    The Hitchhiker’s Guide to Epistasis: the Galactic View

    The world would be simple if it wasn’t for epistasis. Epistasis is the
    wrench in works of evaluating fitness of genotype. Without it the
    Wrightian fitness landscape would be boringly single peaked and the
    task of evolution would be relatively simple. Evolutionary Computation
    is the use of evolution based algorithms for “solving” optimization
    problems. For us, Evolutionary Computation is conscious evolution
    with intent and problem structure, as measured by epistasis, is
    critical to understanding how to better optimize a function. Linkages
    we find in mechanics of real world problems are like the linkages we
    see in biological problems which reveal the chemistry and physics of
    life.

    We will look at a broader view of epistasis from those trying to solve
    optimization problems and how that might be merged with the biological
    prospective. We will discuss the representation of fitness space as
    epistasis space and the implications and limitations for
    computability. We will find, from a theory standpoint, there is some
    good news and some bad news and some wild guesses. So I would like to
    say “in large friendly letters DON’T PANIC.

    Hosted by Dan Weinreich

  • Oct
    18
    2:30pm - 6:30pm

    SciComm Workshop: Communicating Science Effectively

    Science Center/Sciences Library

    The Importance of Message – What is your message?  Why does it matter? This workshop will provide strategies and approaches that will help you to communicate your scientific work effectively to diverse audiences.

    Facilitated by Jory Weintraub, Ph.D., Science Communication Director, Duke Initiative for Science & Society, Duke University. 

    This will be a “hands-on” workshop, so come prepared to practice your skills with your colleagues! There will be 4 sessions: 

    SciComm 101, Delivering Better Presentions, Communication ‘Controversial’ Science and Science Obfuscation and Spin. 

    Please complete this registration form no later than October 16 at 5:00 PM. Register at 
    Please complete this registration form no later than October 16 at 5:00 PM. Register at https://goo.gl/forms/1VLxQWKbPnFg1y1b2

     

    Refreshments provided. 

    Careers, Recruiting, Internships, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Kristin Linn, PhD
    Assistant Professor
    Department of Biostatistics
    University of Pennsylvania


    “Confounding in Imaging-based Predictive Modeling”

    The multivariate pattern analysis (MVPA) of                neuroimaging data typically consists of one or more statistical learning models applied within a broader image analysis pipeline.  The goal of MVPA is often to learn about patterns of variation encoded in magnetic resonance images (MRI) of the brain that are associated with brain disease incidence, progression, and response to therapy.  Every model choice that is made during   image processing and analysis can have implications with respect to the results of neuroimaging studies.  Here, attention is given to two important steps within the MVPA framework: 1) the standardization of features prior to training a supervised learning model, and 2) the training of learning models in the presence of confounding.  Specific examples focus on the use of the support vector machine, as it is a common model choice for MVPA, but the general concepts apply to a large set of models employed in the field.  We propose novel methods that lead to improved classifier performance and interpretability, and we illustrate the methods on real neuroimaging data from a study of Alzheimer’s disease.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Oct
    24
    4:00pm

    Evolutionary Dynamics of Gene Regulation

    Watson Center for Information Technology (CIT)

    Dr. Doug Erwin
    Curator of Paleozoic Invertebrates
    Smithsonian Institution, National Museum of Natural History

    Evolutionary Dynamics of Gene Regulation

    The evolution of gene regulation can be considered from the perspective of a developmental biologist, interested in how regulatory mechanisms and patterns change, and how these changes facilitate evolution of the phenotype. They can also be considered from the standpoint of the evolutionary biologist, interested in the developmental underpinnings of evolutionary patterns.  I will outline the nature of changes in gene regulation at three different levels: 1) the assembly of metazoan regulatory mechanisms during the early history of animals; 2) changes associated with macroevolutionary events such as evolutionary innovations; and 3) changes in gene regulatory networks associated with microevolutionary events such as speciation and adaptive radiations.

    Hosted by Sorin Istrail, in honor of the late Dr. Eric Davidson

  • Oct
    26
    2:00pm - 4:00pm

    Healthcare Needs Innovation. Yours!

    116 Chestnut St Providence, RI 02903

    Presented by the New England Medical Innovation Center (NEMIC) and Advance-CTR

    The New England Medical Innovation Center – NEMIC was created to help open channels of innovation within the community. In this free, 2-hour workshop, we will talk about the importance of innovation to the industry as a whole, different technologies that are driving innovation, human centered design, and cover the components that make innovation fundable from an investors perspective.

    Workshop Agenda:

    • Types of Innovation: Digital Health, Device Development, and Service Improvement
    • Industry Trends & Drivers
    • Elements of Success and Potential Challenges for Innovators
    • About NEMIC & Upcoming Programs

    Coffee and refreshments provided.

    Event is free but registration is required. Register using the link below. 

    Register: https://www.eventbrite.com/e/healthcare-needs-innovation-yours-tickets-50651946335

    Advising, Mentorship, Biology, Medicine, Public Health, Careers, Recruiting, Internships, Entrepreneurship, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Join us for the first Seeing Myself in Science lecture this year with President Christina Paxson in an exchange about her career as an economist and perspectives on diversity and inclusion in the sciences. RSVP here .

    Physical & Earth Sciences
  • This series focuses on skills needed for clear writing in the U.S. academic context. This semester’s workshops will focus on incorporating sources, summarizing, and synthesizing in English.

    Register here .

  • Thomas Serre
    Oct
    31
    4:00pm - 5:00pm

    DSI Colloquia: Thomas Serre

    Watson Center for Information Technology (CIT)
    What are the computations underlying primate versus machine vision?
    Primates excel at object recognition: For decades, the speed and accuracy of their visual system have remained unmatched by computer algorithms. But recent advances in Deep Convolutional Networks (DCNs) have led to vision systems that are starting to rival human decisions. A growing body of work also suggests that this recent surge in accuracy is accompanied by a concomitant improvement in our ability to account for neural data in higher areas of the primate visual cortex. Overall, DCNs have become de facto computational models of visual recognition.
    In this talk, I will review recent work by our group which brings into relief limitations of modern DCNs as computational models of primate vision. I will show that visual features learned by DCNs from large-scale object recognition databases differ markedly from those used by human observers during visual recognition. I will further demonstrate that DCNs are limited in their ability to solve seemingly simple visual reasoning problems involving incremental grouping, similarity and spatial relation judgments suggesting the need for additional neural computations beyond those implemented in current architectures. I will show how neuroscience principles may help guide the future design for more robust computer vision architectures.

    Dr. Serre is Associate Professor in Cognitive Linguistic & Psychological Sciences at Brown University. He received a Ph.D. in Neuroscience from MIT in 2006 and an MSc in EECS from Télécom Bretagne (France) in 2000. Dr Serre is Faculty Director of the Center for Computation and Visualization and the Associate Director of Carney’s behavioral core and the “SmartPlayroom”. Dr Serre has served as an area chair for machine learning and computer vision conferences including CVPR and NIPS. He is currently serving as a domain expert for IARPA’s Machine Intelligence from Cortical Networks (MICrONS) program and as scientific advisor for Vium, Inc. He is the recipient of an NSF early Career award as well as DARPA’s Young Faculty Award and Director’s Award. His research seeks to understand the neural computations supporting visual perception and has been featured in the BBC series “Visions from the Future” and appeared in several news articles (The Economist, New Scientist, Scientific American, IEEE Computing in Science and Technology, Technology Review and Slashdot). 
    Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering
  • Nov
    1
    12:00pm - 1:00pm

    Talk: Thomas Ryden, MassRobotics

    Watson Center for Information Technology (CIT)

    HCRI Design Considerations For Robotics Systems

    Human-Robot interaction design is multidisciplinary effort aimed at understanding and improving the interactions between humans and robots. HRI is no more important than in teleoperated robots, where effective control is critical to the proper experience of these types of robots. But often the human factors elements are given short shrift, either addressed towards the end of the design, or left to just the engineers. During this talk we will look at some of the different elements of HRI – human perception of robots, human factors and human machine interfaces. We will look at how the design requirements of the PackBot impacted the control metaphors and we will examine the design path for the VGo Telepresence Robot.

    Thomas Ryden is the Executive Director of MassRobotics. MassRobotics is a non-profit organization whose mission is to support the robotics community and help grow the next generation of robotics and connected device companies. Prior to joining MassRobotics Mr. Ryden was the founder and CEO/COO of VGo Communications, Inc. While at VGo Mr. Ryden oversaw the development and launch of the VGo telepresence robot. The VGo is used by hospitals, eldercare facilities, schools and other organizations to help people stay better connected, allowing users to essentially be in two places at once. Previously, Mr. Ryden was Director of Sales & Marketing at iRobot Corporation. Under his leadership iRobot secured over $300M in contracts and revenue from its government and industrial products increased from $2M to over $80M annually. In addition Mr. Ryden held roles in program management, overseeing the development of some of iRobot’s most successful products. Mr. Ryden is the co-chairman of the robotics cluster of the Massachusetts Technology Leadership Council and serves on the Mechatronics & Robotics Engineering (MRE) Education Advisory Board, the Tufts University Computer Science Advisory Board, the WPI Robotics Engineering Advisory Board and the Robotics Technical Advisory Panel for ASME. He also is an advisor to a number of robotics start-ups. Mr. Ryden has a B.S. in Electrical Engineering from the University of Vermont and an MBA from Bentley University.

    Host: Professor Stefanie Tellex/HCRI

  • Nov
    2
    12:00pm - 1:00pm

    Sysread Talk: Bernd Fischer, Stellenbosch University

    Watson Center for Information Technology (CIT)

    Seeing Things in the Clouds: Browsing Semi-Structured Data with Tag Clouds over Concept Lattices

    Search is one of the most common operations on the internet, and one of the cornerstones of information retrieval. However, users must know exactly what they want and to formulate explicit queries to search for it. In many applications, this is difficult because the underlying data archives are semi-structured, i.e., consist of a mixture of formatted (e.g., author or date) and free-text (e.g., synopsis or description) fields, and the information remains implicit.

    Software development archives such as revision control and issue tracking systems are one instance of this general problem. Users are interested in high-level questions (e.g., “How have the active developers changed over time?”, “Which topics has this developer been working on?”, or “Which methods are often changed together?”) but the archives do not directly provide the answers. Dedicated mining tools can automate the search for specific aspects, but remain inflexible. We instead propose an interactive browsing approach to analyze such semi-structured data archives. The main novelty of this approach is the combination of an intuitive tag cloud interface with an underlying concept lattice that provides a formal structure for navigation. This lets users serendipitously explore the data without predefined goals and along different navigation paths, and thereby allows them to discover unexpected information.

    We implemented our approach in the ConceptCloud browser. It uses tag clouds to provide a unified view of the selected items, but the browser interface also supports concurrent navigation in multiple interlinked tag clouds that can each be customized individually, which allows multifaceted archive explorations. We sketch the mathematical foundations and show how we used ConceptCloud in different domains, including software archives, citation data, and wine reviews.

    Bernd Fischer is a professor in the Division of Computer Science at Stellenbosch University (South Africa) and the current Head of Division. His research area is automated software engineering, in particular logic-based (in the broadest sense) techniques. He has worked on a wide range of topics, such as specification-based component reuse, program synthesis from high-level models, or software model checking.

    Fischer studied Computer Science at the TU Braunschweig (Germany) but obtained his PhD from the University of Passau (Germany) in 2001. From 1998 to 2006, he was a Research Scientist with USRA/RIACS at the NASA Ames Research Center (USA). From 2006 to 2013, he held a position as Senior Lecturer at the University of Southampton (UK), and in 2013, he moved to Stellenbosch University (South Africa), in an attempt to improve the access to both sunshine and wine after seven years in England.

    Hosts: Professor Shriram Krishnamurthi, Sysread

  • Nov
    2
    3:00pm - 5:00pm

    Thesis Defense: Alexander Galakatos

    Watson Center for Information Technology (CIT)

    Accelerating Interactive Data Exploration

    The widespread popularity of visual data exploration tools has empowered domain experts in a broad range of fields to make data-driven decisions. However, a key requirement of these tools is the ability to provide query results at “human speed,” even over large datasets. To meet these strict requirements, we propose a complete redesign of the interactive data exploration stack. Instead of simply replacing or extending existing systems, however, we propose an Interactive Data Exploration Accelerator (IDEA) that connects to existing data management infrastructures in order to speed up query processing for visual data exploration tools.

    In this redesigned interactive data exploration stack, where an IDEA sits between a visual data exploration tool and a backend data source, several interesting opportunities emerge to improve interactivity for end users. In this context, we first propose a novel approximate query processing formulation that better models the conversational interaction paradigm promoted by visual data exploration tools. Since the time dimension is often a critical component of many datasets, we then present a detailed survey of existing backend data sources specifically designed for time-dependent data in the context of interactive data exploration. Finally, based on the results from our study, we propose a new approximate index structure for an interactive data exploration stack that leverages the trends that exist in the underlying data to improve interactivity while significantly reducing the storage footprint.

    Host: Professor Tim Kraska

  • José R. Zubizaretta, PHD
    Associate Professor, Department of Health Care Policy, Harvard Medical School
    Faculty Affiliate, Department of Statistics , Faculty of Arts and Sciences
    Harvard University

    “Building Representative Matched Samples with Multi-valued Treatments in Large Observational Studies”

     In observational studies of causal effects, matching methods are widely used to approximate the ideal study that would be conducted under controlled experimentation. In this talk, I will discuss new matching methods that use tools from modern optimization to overcome five limitations of standard matching approaches. In particular, these new matching methods (i) directly obtain flexible forms of covariate balance, as specified before matching by the investigator; (ii) produce self-weighting matched samples that are representative of target populations by design; and (iii) handle multiple treatment doses without resorting to a generalization of the propensity score. (iv) These methods can handle large data sets quickly. (v) Unlike standard matching approaches, with these new matching methods, usual estimators are root-n consistent under usual conditions. I will illustrate the performance of these methods in a case study about the impact of a natural disaster on educational opportunity.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Nov
    6

    Deep Learning for Science

    In recent years, Deep Learning has revolutionized the fields of computer vision, speech recognition and control systems. Can Deep Learning have a positive impact on domain sciences?

    The talk will review NERSC’s current and future strategy in Deep Learning. We will review a range of use cases spanning Climate, Cosmology, Astronomy, High-Energy Physics, Neuroscience and Genomics. We will briefly touch upon efforts in scaling Deep Learning on petascale and exascale platforms in DOE. We will conclude with a list of open challenges and speculations about the role of AI in scientific discovery in the future.

    Prabhat leads the Data and Analytics Services team at NERSC. His current research interests are in machine learning, statistics and large scale data analytics. He has broad interests in data management, visualization and HPC. Prabhat received an ScM in Computer Science from Brown University (2001) and a B.Tech in Computer Science and Engineering from IIT-Delhi (1999). He is currently pursuing a PhD in the Earth and Planetary Sciences Department at U.C. Berkeley.

    Host: Professor David Laidlaw

  • Dr. Ute Deichmann
    Director, Jacques Loeb Centre for the History and Philosophy of the Life Sciences
    Ben-Gurion University of the Negev

    From Gregor Mendel to Eric Davidson: Models in experimental biology, their epistemologies and basic biological principles.

    Models have been widespread in biology since its emergence as a modern experimental science in the 19th century. Typical model types include animal models, chemical models and mathematical models. Quantitative models in biology differed strongly among researchers in various fields regarding function and explanatory power.

    This talk presents the properties and epistemological basis of pertinent mathematical models in developmental biology and heredity from the 19th to the 21st centuries. (Models in physiology, ecology, population genetics and origin of life and artificial life research are not included.) I show that the models differ not only in their epistemologies but also in how they explicitly or implicitly take into account basic biological principles, in particular those of biological specificity (which was in part replaced by genetic information) and genetic causality.

    I argue that models that disregarded these principles did not impact the direction of biological research in a lasting way and that successful models were based on inductive as well as hypothetico-deductive methodology; they were not mathematical descriptions or simulations of biological phenomena. Moreover, I argue that though the recent availability of big data technologies tremendously facilitates systems approaches and pattern recognition, inductive and hypothetico-deductive experimental methodologies have remained fundamentally important as long as causal-mechanistic explanations of complex systems are pursued.

    Hosted by Sorin Istrail, in honor of the late Dr. Eric Davidson

  • Nov
    7
    5:00pm

    Google Design Workshop

    Barus and Holley

    Google’s design thinking exercise helps students to practically apply their code and has proven to be highly beneficial for those that are currently interviewing for product manager roles across the industry. 

    Careers, Recruiting, Internships
  • A little more than three years ago, while attending the Conference for African American Researchers in the Mathematical Sciences at ICERM, I spontaneously announced to ICERM Associate Director Ulrica Wilson that I thought I would write a book about the heart of mathematics. Then I went ahead and did it. What was I thinking?! Publishing Mathematics: Rhyme and Reason is akin to undressing publicly. So, what ends up being exposed? Well, among other things, I place in plain view relationships with people in my mathematical upbringing, some of whom popped into my life for better and, at least once, for worse. One will also see my life-long attachment to the simple truths of mathematics. The book is a message to the kid I was, with the assumption that such kids still exist. I present a large collection of theorems and call them nursery rhymes in the book, though I didn’t stumble across a few of them until I was well beyond nursery-rhyme age. I also write about whether or not I have ever managed to scratch the surface of mathematics. I plan to spend the allotted time discussing these matters and more. Come one, come all, we’re going to have a ball. This is a pre-conference event for the Blackwell-Tapia Conference and is open to the public. About the Speaker After receiving a BA degree in Mathematics and Economics from Yale College in 1970, Dr. Mel Currie worked in his hometown Pittsburgh as an economic analyst with Gulf Oil Corporation. He then spent three years in Germany, where he taught algebra in the Düsseldorf public school system. After returning from Germany, he earned MA and PhD degrees in mathematics at the University of Pittsburgh. Seven years in academia followed graduate school. Dr. Currie joined the National Security Agency in 1990 and in 1994 became chief of the Crypto-mathematical Applications Branch. He moved to the Cryptography Office in 1996, where he was Chief of the Cryptographic Research and Design Division, the Codemakers. His position at retirement was Research Advocate reporting directly to the Director of Research, NSA. He is the recipient of many awards. In particular, he was the inaugural recipient of NSA’s Crypto-Mathematics Institute’s Leadership Award.

    Mathematics, Technology, Engineering
  • Nov
    8
    6:30pm

    Master’s Student Game Night

    Watson Institute for International and Public Affairs
    Graduate School, Postgraduate Education
  • Nov
    9
    1:00pm - 3:00pm

    Thesis Defense: Yeounoh Chung

    Watson Center for Information Technology (CIT)

    Quantifying Uncertainty in Data Exploration

    In the age of big data, uncertainty in data constantly grows with its volume, variety and velocity. Data is noisy, biased and error-prone. Compounding the problem of uncertain data is uncertainty in data analysis. A typical end-to-end data analysis pipeline involves cleaning and processing the data, summarizing different characteristics and running more complex machine learning algorithms to extract interesting patterns. The problem is that, all the steps are error-prone and imperfect. From the input data to the output results, uncertainty propagates and compounds. This thesis addresses such problems of uncertainty in data analysis.

    First, it is shown how uncertainty in a form of missing unknown data items can affects aggregate query results, which are very common in exploratory data analysis. It is challenging to make sure that we have collected all the important data items to derive correct data analysis, especially when we deal with real-world big data; there is always a chance that some items of unknown impacts are missing from the collected data set. To this end, sound techniques to derive aggregate queries with the \emph (the data set may or may not be complete) are proposed.

    Next, uncertainty in the form of data errors is examined. It is almost guaranteed that any real-world data sets contain some forms of data error. This is an important source of uncertainty in data analysis because those errors would almost surely corrupt the final analysis results. Unfortunately, there has not been much work to measure the quality of data in terms of the number of remaining data errors in the data set. The data cleaning best practices basically employ a number of (orthogonal) data cleaning techniques, algorithms or human/crowd workers in a hope that the increased cleaning efforts would result in a perfectly clean data set. To guide such cleaning efforts, techniques to estimate the number of undetected remaining errors in a data set are proposed.

    Lastly, a couple of uncertainty problems related to machine learning (ML) model quality are addressed.  ML is one of the most popular tools for learning and making predictions on data. For its use, ensuring good ML model quality leads to more accurate and reliable data analysis results. The most common practice for model quality control is to consider various test performance metrics on separate validation data sets; however, the problem is that the overall performance metrics can fail to reflect the performance on smaller subsets of the data. At the same time, evaluating the model on all possible subsets of the data is prohibitively expensive, which is one of the key challenges in solving this uncertainty problem.
    Furthermore, missing unknown data items can also degrade the quality of the model. The challenge is that most ML/inference models can perform badly on unseen instances, if similar cases are not learned during training.

    Host: Professor Tim Kraska

  • Nov
    13
    3:00pm - 4:30pm

    Self-Editing Techniques for Science Writers

    Sciences Library

    Tuesday, November 13, 2018, 3:00-4:30 PM, SciLi 520

    Led by Dr. Theresa M.  
    Desrochers, Assistant Professor of Neuroscience & Psychiatry and Human  
    Behavior, this workshop will focus on the fundamentals of scholarly writing  
    in the sciences. Workshop participants are expected to come prepared with  
    their own writing and will spend time editing. Registration is required.

  • Nov
    13
    6:00pm - 7:30pm

    Lyft Information Session

    CareerLab
    • Lyft will be on campus to share more about their core and self-driving teams! We are actively recruiting for 2019 internships and full-time positions across Software Engineering, Hardware Engineering, Data Science, and more!
    Careers, Recruiting, Internships
  • Nov
    14
    12:00pm - 1:00pm

    Succeeding in Graduate School (Sciences)

    CareerLab

    Wondering how to manage your time, choose your courses, build relationships with your advisers, or get involved with organizations on campus?  Join CareerLAB for tips and strategies for succeeding in graduate school! This event will feature your peers–advanced graduate students–sharing what they have learned about how to make the most of your time here.

    Careers, Recruiting, Internships, Graduate School, Postgraduate Education, Training, Professional Development
  • DeDe Paul
    Nov
    14
    4:00pm - 5:00pm

    DSI Colloquia: DeDe Paul

    Watson Center for Information Technology (CIT)

    AN INSIDER’S VIEW OF A DATA SCIENCE RESEARCH LAB

    We in the Data Science and AI Research organization in AT&T Labs are fortunate to be involved the company’s Machine Learning and AI transformation - which greatly depends on Data Science expertise. I will provide a high-level view of the methodologies and applications associated with some of our active research areas. Then I will showcase a current project to demonstrate a data science workflow and the importance of ongoing professional development in the field. 

  • Stochastic Cinema: The Intersection of Randomness and Design

    This talk will examine two case studies on the use of random numbers to not only influence, but enhance design in feature film production. The first will be a geometric application of probability from Incredibles 2, followed by a stochastic illumination strategy employed on Coco.

    Designing cities is implicitly a task of tremendous scale. But how can we keep design nimble as story and art direction are changing every day? In this part of the talk, we will start by looking at the artistic inspiration for Municiberg and New Urbem, and the design challenges involved in creating two distinct cities. Then we will see how this influenced the creation of the city generation tool, and learn about how probability can allow both rapid prototyping of large scale sets, as well as achieve a polished, final look.

    In the second half, we will move towards illumination. On Coco, there were many glowing creatures, called alebrije, added to the magical Land of the Dead. Similar to before, we will start by looking at some of the artistic exploration for what the alebrije would look like in our world. Then we will examine the social implications of what happens when we try to let a department that isn’t lighting control something on the screen that lights up. And finally we will see how a novel stochastic rendering technique was employed to keep everyone happy.

    This talk is designed to be enjoyable even with no previous knowledge (or interest) of probability or computer science.

    Host: Professor Barbara Meier

  • Pratik Shah, PhD
    Nov
    19

    Novel machine learning and pragmatic computational medicine approaches to improve health outcomes for patients.

    Abstract:

    In the first part of the talk, I describe machine learning algorithms and methods to identify disease characteristics such as physician labels, fluorescent biomarkers and histological dyes from simple white light imagesIn the second part, I share novel phase 2 and 3 clinical trials designed by unorthodox “self-learning” AI and machine learning systems to accelerate clinical development to bring new innovations and advances to patients safely. Our clinical trial designs achieves significant reduction in tumor sizes during oncology care, and offers personalized and precision dosing recommendations for individual patients. We conclude by outlining a strategic plan to conduct clinical trials with devices, algorithms and real world evidence in accordance with the 21st Century Cures Act.

     
    Bio : Dr. Pratik Shah is a Principal Research Scientist at The MIT Media Lab and leads the Health 0.0 research program. His research creates novel intersections between engineering, medical imaging, machine learning, and medicine to improve health outcomes for patients. Ongoing research areas: 1) artificial intelligence and machine learning methods for the detection of cancer biomarkers using standard photographs vs. expensive medical images; 2) unorthodox artificial intelligence algorithms to design optimal and faster clinical trials to reduce adverse effects on patients; and 3) point-of-care medical technologies for real world data and evidence generation to improve public health. Past acknowledgments include the American Society for Microbiology’s Raymond W. Sarber National Award, a Harvard Medical School and Massachusetts General Hospitals ECOR Fund for Medical Discovery postdoctoral fellowship, coverage by leading national and international news media outlets. Dr. Shah has been an invited discussion leader at Gordon Research Seminars; a speaker at Cold Spring Harbor Laboratories, Gordon Research Conferences, and IEEE bioengineering conferences; and a peer reviewer for leading scientific publications and funding agencies. Pratik has a BS, MS, and a PhD in biological sciences and completed fellowship training at Massachusetts General Hospital, The Broad Institute of MIT and Harvard, and Harvard Medical School. 
    Biology, Medicine, Public Health, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Nov
    22

    Thanksgiving Day Holiday

  • Applied Math Colloquium

    Wednesday, November 28, 2018
    12:00pm
    Room 190, Barus & Holley

    Michael Jordan , University of California, Berkeley

    Title

    Abstract

  • PAT STEELE
    Nov
    28
    4:00pm - 5:00pm

    DSI Colloquia: Patrick Steele

    Watson Center for Information Technology (CIT)

    Title and Abstract TBD

  • Robert J. & Nancy D. Carney Institute for Brain Science
    Carney Institute Lunch Talk

    Thomas Serre, PhD
    Director for the Center for Computation and Visualization, Associate Professor of Cognitive, Linguistic and Psychological Sciences
    -and-
    David Borton, PhD
    Assistant Professor of Engineering
    Brown University
    “Dealing with Brain Data”

    Thu, Nov 29
    12noon - 1:30pm

    Chancellor’s Dining Room, Sharpe Refectory
    Please RSVP
    *seating is limited*
    Biology, Medicine, Public Health, Psychology & Cognitive Sciences
  • Nov
    30
    9:00am - 12:00pm

    COBRE CBC: QC and Exploratory Data Analysis

    Watson Center for Information Technology (CIT)

    RNA-seq QC and Exploratory Data Analysis Using Bioconductor Workshop


    The COBRE Computational Biology Core is running an updated variant of the Bioconductor workshops originally presented by Levi Waldron of the Bioconductor core team.  We will present the material in two parts:

    Part 1: QC and Exploratory Data Analysis - November 30th, 2018

    Part 2: Differential Expression Analysis - TBA (~February 2019)

    These workshops will feature an optional new aspect: the use of participant data.  Attendees will, under certain conditions, be able to utilize their own data for the tutorial.  Data that can be used in this capacity is RNA-seq data with one intervention that has two levels (treated v. untreated).  Anyone without suitable data will be able to use sample data that will be provided the day of the workshop.

    If you wish to attempt to use your own data in the workshop, please contact [email protected]  by no later than November 16th so we can verify that the data is suited for this purpose.  This message should be in addition to your registration using the survey link below.

    This workshop will be held on Friday, November 30, 2018, in the CIT SWIG Board Room (9 AM - 12 PM). 

          Please register here if you would like to participate (requires a Brown-affiliated account):

    https://goo.gl/forms/CkTFvECrMjzRpxG42

    Please do not hesitate to contact us at [email protected]

    or through our website: https://brown.edu/cis/data-science/compbiocore/

    Regards,

    Brown Computational Biology Core

  • Ellie Pavlick
    Dec
    5
    4:00pm - 5:00pm

    DSI Colloquia: Ellie Pavlick

    Watson Center for Information Technology (CIT)

    Title and Abstract TBD

  • Yakov Shlapentokh-Rothman

    Title:

    Abstract

    Lauren K. Williams , Harvard University

    Title

    Abstract

  • Algorithms: Theory meets Practice

    Theory has an important role to play in computing practice. The faster speeds and larger memory sizes of current computers vastly increase the size of the design space of possible solutions for even simple problems. A theoretical approach to algorithm design helps to systematically explore this space. Such an approach can produce algorithms that are correct and efficient as well as simple to program. I shall illustrate these points with one or two real-life examples.

    Robert E. Tarjan is the Distinguished University Professor of Computer Science at Princeton University and Chief Scientist of Intertrust Technologies. He received his B.S. from the California Institute of Technology in 1969 and his M. S. and Ph.D. from Stanford University in 1971 and 1972. He has held academic positions at Cornell, Berkeley, Stanford, and NYU, and industrial research positions at Bell Labs, NEC, HP, and Microsoft. Among other honors, he received the Nevanlina Prize in Informatics, given by the International Mathematical Union, in 1982, the A.C.M. Turing Award in 1986, and the A. C. M. Paris Kanellakis Award in Theory and Practice in 1999, the last together with Daniel Sleator for their invention of splay trees. He is a member of the National Academy of Sciences and the National Academy of Engineering, and a Fellow of the American Philosophical Society and the American Academy of Arts and Sciences. He has published over 250 papers, mostly in the areas of the design and analysis of data structures and graph and network algorithms. In 2018 he was named one of the “50 Most Influential Living Computer Scientists” by TheBestSchools.org.

    ********************************

    This lecture series honors Paris Kanellakis, a distinguished computer scientist who was an esteemed and beloved member of the Brown Computer Science Department. Paris joined the Computer Science Department in 1981 and became a full professor in 1990. His research area was theoretical computer science, with emphasis on the principles of database systems, logic in computer science, the principles of distributed computing and combinatorial optimization. He died in an airplane crash on December 20,1995, along with his wife, Maria Teresa Otoya, and their two young children, Alexandra and Stephanos Kanellakis.

    A reception will follow.

    Host: Professor Philip Klein

  • Introduction to the asymmetric simple exclusion process (from a combinatorialist’s point of view)  

    Lauren K. Williams
    Harvard, Department of Mathematics

    Abstract: 
    The asymmetric simple exclusion process (ASEP) is a model of particles hopping on a one-dimensional lattice, subject to the condition that there is at most one particle per site.  This model was introduced in 1970 by biologists (as a model for translation in protein synthesis) but has since been shown to display a rich mathematical structure.  There are many variants of the model – e.g. the lattice could be a ring, or a line with open boundaries.  One can also allow multiple species of particles with different “weights.”  I will explain how one can give combinatorial formulas for the stationary distribution using various kinds of tableaux.  I will also explain how the ASEP is related to interesting families of orthogonal polynomials, including Askey-Wilson polynomials, Koornwinder polynomials, and Macdonald polynomials.  Based on joint work with Sylvie Corteel (Paris) and Olya Mandelshtam (Brown).

  • Dec
    12
    4:00pm

    “Integrating Genomic Data into the Electronic Health Record”

    Watson Center for Information Technology (CIT)

    Dr. Nephi Walton
    Genomic Medicine, Geisinger Health Systems

    “Integrating Genomic Data into the Electronic Health Record”

    As the era of precision medicine is upon us and more patients are having genetic sequencing performed, our health technology infrastructure remains largely inadequate in its ability to harness and utilize genomic data.  Of the gigabytes of information generated from sequencing one patient, the information that enters the clinical realm represents only a small fraction of the useable information and even that information is not stored in a way that it can be utilized electronically to support patient care.  There are many challenges surrounding the use of genomic data in the electronic health record. In this session we will address the challenges of providing clinical data in the EHR and how we can facilitate this data in the clinical workflow to provide information to both patients and providers to help manage care based on genomics. 

    Hosted by Will Fairbrother

  • Dec
    13
    12:00pm - 1:00pm

    Talk: Anna Gommerstadt, Carnegie Mellon University

    Watson Center for Information Technology (CIT)

    Teaching Talk: Sorting Arrays

    Sorting algorithms are frequently taught in data structures courses to expose students to algorithmic thinking and asymptotic complexity. In this lecture, we will introduce the selection sort algorithm. As we implement selection sort, we will use program annotations (contracts) to define and reason about the correctness of our code. Time permitting, we will formally prove the correctness of our selection sort implementation. We will also review big-O notation to determine its running time.

    Hannah (Anna) Gommerstadt is a PhD candidate in the Computer Science Department at Carnegie Mellon University where she works with Frank Pfenning and Limin Jia. Her research interests are at the intersection of programming languages and security especially the use of language-based and logic-based methods to provide formal security guarantees. Her current work focuses on dynamic monitoring and contract enforcement in a concurrent setting. She obtained her B.A. in Computer Science and Mathematics from Harvard University.

    Host: Professor Kathi Fisler

  • Dec
    14
    4:00pm - 5:00pm

    Retirement Lecture: John Savage

    Watson Center for Information Technology (CIT)

    Cybersecurity: A Grand Challenge for the Society

    The Department of Computer Science will host Professor John Savage on Thursday, December 13, 2018, at 4pm in CIT 368. Savage has spent 51 years at Brown and became Professor Emeritus in July 2018. To commemorate this occasion, he will give a lecture entitled “Cybersecurity: A Grand Challenge for the Society”. 

    The cyber security problem is now a grand challenge problem that impacts all aspects of life. Making cyberspace safe is a complex problem that requires the engagement of all segments of society.

    Dr. John E. Savage is the An Wang Emeritus Professor of Computer Science at Brown University. He earned his PhD in Electrical Engineering at MIT in 1965, joining Bell Laboratories in the same year. He moved to Brown University in 1967, co-founded the Department of Computer Science, and served as its second chair from 1985 to 1991. He has served Brown in many capacities including as Chair of the Faculty Executive Committee and Chair of the 2002-2003 Task Force on Faculty Governance. He was a member of the MIT Corporation Visiting Committee for EECS from 1991-2002. In 2011 he gave Congressional testimony on cyber security before the Judiciary Subcommittee on Crime and Terrorism of the United States Senate. He has done research on coding and communication theory, theoretical computer science, VLSI theory, silicon compilation, scientific computing, computational nanotechnology, the performance of multicore chips, reliable computing with unreliable elements, blockchain governance, and cyber security policy.

    He is a Guggenheim Fellow and a Fellow of AAAS and ACM, and a Life Fellow of IEEE. He served as Jefferson Science Fellow in the U.S. Department of State in 2009-2010. He is a Professorial Fellow at the EastWest Institute, a member of the Board of the Michael Dukakis Institute, and a member of the Board of Advisors of the Verified Voting Foundation. He served as a member of the Rhode Island Cybersecurity Commission and participated in the 2016 Munich Security Conference as a member of the McCain-Whitehouse U.S. Congressional Delegation.

    Reception to follow.

    Host: Professor Ugur Cetintemel

  • Causal and mathematical models are widely used for decision making and policy evaluation at both the micro and macro levels. For example, causal models using large datasets are used to evaluate treatment efficacy in HIV; mathematical models are used to simulate the effects of prevention or policy measures to improve health outcomes or reduce the spread of infectious diseases. Entities such as the World Health Organization and UNAIDS rely on these models to set wide-ranging and high-impact policy related to treatment and prevention of infectious disease.

    Causal models tend to rely on large-scale cohort data, while mathematical models in many ways represent evidence synthesis. Important methodologic issues in the development, application, and interpretation of these models include the role of untestable assumptions, transportability of findings to specific populations of interest, model calibration and validation, and uncertainty quantification. The datasets used to develop these models are complex in nature.

    This workshop will bring together leading researchers in the areas of modeling, machine learning and causal inference to delve more deeply into foundational and methodologic issues and their implications, illustrate the use of these models in real-world settings, and draw connections between the two approaches.

    Key questions to be addressed and discussed include:

    • What is the role of an underlying causal model in decision making?
    • How do we quantify uncertainty from multiple sources, such as model selection, untestable assumptions, prediction uncertainty?
    • What is the role of predictive models in causal inference?
    • What are the connections between statistical models and mathematical agent-based models for drawing causal inferences?
    • What is the role of machine learning in inferring causal relationships and decision making?
    • How should methods be adapted to specialized settings (e.g., social networks)?
  • Jan
    22
    8:00am - 4:30pm

    Advance-CTR Mentoring Training Program

    University of Rhode Island

    Join Advance-CTR for the next installment of our highly rated Mentoring Training Program on January 22, 2019 at the University of Rhode Island. 

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

    The program targets faculty who mentor junior investigators who conduct clinical and translational research, which is broadly defined. Preference will be given to more senior mentors.

    Registration is required. Learn more about the training program and how to register on AdvanceCTR.org .

    Advising, Mentorship, Biology, Medicine, Public Health, Careers, Recruiting, Internships, Research, Training, Professional Development
  • Jan
    25
    9:00am - 10:30am

    REDCap Workshop: How to Plan for Your Study

    Rhode Island Hospital

    This workshop will focus on introductory concepts for getting your project started in REDCap. Participants will learn basic concepts and best practices for building databases in the context of their clinical research questions. The small class size will allow for individuals to ask specific questions about their individual project.

    This workshop is complimentary but registration is required as space is limited. Please reserve your seat by clicking on the link below. 

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Jan
    28
    9:00am

    Scientific Machine Learning

    121 South Main Street

    The machine learning revolution is already having a significant impact across the social sciences and business but it is also beginning to change computational science and engineering in fundamental and very varied ways. We are experiencing the rise of new and simpler data driven methods based on techniques from machine learning such as deep learning. This revolution allows for the development of radical new techniques to address problems known to be very challenging with traditional methods and suggests the potential dramatic enhancement of existing methods through data informed parameter selection, both in static and dynamic modes of operation. Techniques are emerging that allows us to produce realistic solutions from non-sterilized computational problems in diverse physical sciences. However, the urgent and unmet need to formally analyze, design, develop and deploy these emerging methods and develop algorithms must be addressed. Many central problems, e.g. enforcement of physical constraints in machine learning techniques and efficient techniques to deal with multiscale problems, are unmet in existing methods. The primary goal of this Hot Topic workshop is to bring together leading researchers across various fields to discuss recent results and techniques at the interface between traditional methods and emerging data driven techniques to enable innovation in scientific computing in computational science and engineering.

    Mathematics, Technology, Engineering
  • Please join the COBRE Center for Computational Biology of Human Disease, the Center for Computational Molecular Biology, and the Data Science Initiative Group for our weekly Wednesday Seminar.  

    COBRE CBHD is hosting Dr. Uri Hershberg on Wednesday, January 30, 2019, from 4 to 5 p.m., in the CIT Building (115 Waterman Street) SWIG Boardroom (Room 241) as he presents a talk entitled:

    History will teach us something: Immune system examples of the effects of different scales of selection pressure

     About the Speaker

    Uri Hershberg leads the Systems Immunology Lab, which is one of the leading labs in the world for the study and computational analysis of B cell receptor repertoires. SImLab has developed several tools for analysis and modeling of immunology experiments used for research published in Nature Immunology, Nature, and other leading journals. Most importantly, they have developed ImmuneDB (immuneDB.com ), a tool for rapid database creation, analysis, visualization, and dissemination of high throughput B cell receptor and T cell receptor sequencing experiments. In recent work, published in Nature Biotechnology, Dr. Hershberg has used ImmuneDB to help in the first study of B cell repertoires in human tissues, leading to the discovery that the gut and blood tissues have orthogonal B cell receptor repertoires.

    Recent Key Publications

    Yaari, G., Flajnik, M., and Hershberg, U. (2018) Questions of Stochasticity and Control in Immune Repertoires. Trends in Immunology, 39(11):859-861. PDF

    Rosenfeld, A.M., Meng, W., Luning Prak, E.T., Hershberg, U. (2018) ImmuneDB, a Novel Tool for the Analysis, Storage, and Dissemination of Immune Repertoire Sequencing Data. Frontiers in Immunology, 2018;9: 2107. 

    Meng, W*., Zhang, B*., (*authors contributed equally), Schwartz, G.W., Rosenfeld, A.M., Ren, D.,Thome, J.J.C., Carpenter, D.J., Matsuoka, N., Lerner, H., Friedman, A.L.,  Granot, T., Farber D.L., Shlomchik, M.J.,  Hershberg, U.*  and Luning Prak, E.T.*   (*corresponding authors) (2017) An atlas of B-cell clonal distribution in the human bodybNature Biotechnology, 35(9):879–884, PMCID: PMC5679700.


    Yucel, M., Muchnik, L., and Hershberg, U. (2016) Detection of network communities with memory-biased random walk algorithms. Journal of Complex Networks, 5(1), 48-49. PDF

     Hershberg, U. and Luning Prak, E. T.  (2015) The analysis of clonal expansions in normal and autoimmune B cell repertoiresPhil. Trans. R. Soc. B,  370: 20140239. PMCID: PMC4528416.

    Hershberg, U. and Shlomchik, M. J. (2006) Differences in potential for amino acid change following mutation reveals distinct strategies for kappa and lambda light chain variationPNAS, 103(43):15963-8, PMCID:PMC1635110.

  • Rani Elwy, PhD

    Associate Professor

    Director, Implementation Science Core

    Department of Psychiatry and Human Behavior

    Alpert Medical School of Brown University

     

    Wednesday, February 6, 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 important elements of practice-based research networks, pragmatic trials and population health principles; Describe the rationale for developing pragmatic trials and practice-based designs for answering translational research questions; and to Compare and contrast the scientific evidence for different forms of complementary and integrative health approaches (CIH), such as tai chi, yoga, or meditation, for pain and post-traumatic stress

     Disclosure: Dr. Elwy has no financial relationships to disclose.

  • Feb
    6
    4:00pm

    DSI Colloquium: Jennifer Van

    164 Angell, 3rd Floor

    “Data Science in the Wild: What I Wish I Knew About Data Science in Industry Before I Started”

    Ask any two industry data scientists what they do for their job and they will both tell you completely different responses. Come to this firsthand talk to hear about the different types of technical problems and career paths that a data scientist has experienced across Fortune 500 companies and small start-ups.

    Jennifer Van has degrees in Math and Computer Science from the University of Virginia. She has worked as a research scientist in computational biology labs, a research scientist on explainable artificial intelligence and the ethics of machine learning at financial institutions, and now currently works at Mapbox, a start-up, as an N LP engineer.

  • Feb
    7
    4:00pm

    Data Science Talk: Grant Rotskoff

    170 Hope Street

    Neural networks as interacting particle systems: Understanding global convergence of parameter optimization dynamics

    The performance of neural networks on high-dimensional data distributions suggests that it may be possible to parameterize a representation of a target high-dimensional function with controllably small errors, potentially outperforming standard interpolation methods. We demonstrate, both theoretically and numerically, that this is indeed the case. We map the parameters of a neural network to a system of particles relaxing with an interaction potential determined by the loss function. This mapping gives rise to a deterministic partial differential equation that governs the parameter evolution under gradient descent dynamics. We also show that in the limit that the number of parameters n is large, the landscape of the mean-squared error becomes convex and the representation error in the function scales link n^ . In this limit, we prove a dynamical variant of the universal approximation theorem showing that the optimal representation can be attained by stochastic gradient descent, the algorithm ubiquitously used for parameter optimization in machine learning. This conceptual framework can be leveraged to develop algorithms that accelerate optimization using non-local transport. I will conclude by showing that using neuron birth/death processes in parameter optimization guarantees global convergence and provides a substantial acceleration in practice.

    Mathematics, Technology, Engineering
  • Please join the COBRE Center for Computational Biology of Humand Disease, the Center for Computational Molecular Biology, and the Data Science Initiative for the COBRE CBHD Seminar on Wednesday, February 13, 2019, from 4 p.m. to 5 p.m. 

    Jason Wood, PhD from the MCB Department will present.

    Title: The Role of Sirtuins in Neurodegenerative Disease

  • Feb
    14
    8:00am - 11:30am

    REDCap Class: Creating Databases & Using Surveys

    Rhode Island Hospital

    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. Attendees are encouraged to bring laptops but this is not be required.

    This class is complimentary but registration is required as space is limited. Reserve your spot by clicking on the link below. 

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • This week’s workshop will be Part 2 of “Deep Learning with Keras/Tensorflow” for intermediate programmers. This is part of a weekly series of workshops on basic techniques and skills in data science. Open to all members of the Brown community; lunch is provided. Please register! For more information and to register, see the DSI website. 

    Mathematics, Technology, Engineering, Physical & Earth Sciences, Psychology & Cognitive Sciences, Research
  • Theory and Practice in Machine Learning and Computer Vision Feb 18 - 22, 2019 Recent advances in machine learning have had a profound impact on computer vision. Simultaneously, success in computer vision applications has rapidly increased our understanding of some machine learning techniques, especially their applicability. This workshop will bring together researchers who are building a stronger theoretical understanding of the foundations of machine learning with computer vision researchers who are advancing our understanding of machine learning in practice. Much of the recent growth in the use of machine learning in computer vision has been spurred by advances in deep neural networks. At the same time, new advances in other areas of machine learning, including reinforcement learning, generative models, and optimization methods, hold great promise for future impact. These raise important fundamental questions, such as understanding what influences the ability of learning algorithms to generalize, understanding what causes optimization in learning to converge to effective solutions, and understanding how to make optimization more efficient. The workshop will include machine learning researchers who are addressing these foundational questions. It will also include computer vision researchers who are applying machine learning to a host of problems, such as visual categorization, 3D reconstruction, event and activity understanding, and semantic segmentation.

    Mathematics, Technology, Engineering
  • Feb
    20
    5:00pm - 7:00pm

    Algorithmic Justice: Race, Bias, and Big Data

    Institute at Brown for Environment & Society (IBES)

    Data are not objective; algorithms have biases; machine learning doesn’t produce truth. These realities have uneven effects on people’s lives, often serving to reinforce existing systemic biases and social inequalities. At the same time, data can be used in the service of social justice, and taking control of the data produced about people and its use is more and more important in our data-centric world. Brown’s Center for the Study of Race and Ethnicity in America (CSREA) and Data Science Initiative (DSI) invite you to a panel discussion about algorithms, race, and justice—the problems and the possibilities.

    Speakers
    • Meredith Broussard, Assistant Professor of Journalism, Arthur L. Carter Journalism Institute, New York University
    • Max Clermont ’11, MPH ’12, Co-founder, Head of Policy, Data for Black Lives
    • Virginia Eubanks, Associate Professor of Political Science, University of Albany, SUNY
    • Yeshimabeit Milner ’12, Founder and Executive Director, Data for Black Lives
    • Samuel Sinyangwe, Co-Founder, Mapping Police Violence and Campaign Zero

    Presented by the Data Science Initiative and the Center for the Study of Race and Ethnicity in America at Brown University.

    Free and open to the public. Light reception to follow. 

  • Feb
    28
    12:00pm - 1:30pm

    Advance-CTR Biostatistics Core Seminar Series

    Brown University Medical Education Building (Alpert Medical School)

    Presented by the Research Design, Epidemiology, and Biostatistics Core of Advance-CTR, this bi-monthly Seminar Series offers instruction on data analysis (first Tuesday of each month), and study design (last Thursday of each month). The Seminars are available to attend in-person, or remotely via Zoom. 

    Jess Kaminsky: “Overview of Study Design”

    Cursory description of experimental vs. observational studies, including ecological studies, descriptive studies, cross-sectional studies, case-control studies, cohort studies, case reports/series.

    Register to watch remotely .

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Mar
    1
    9:00am - 12:00pm

    CBC Visualization in R with tidyverse and ggplot2 Workshop

    Watson Center for Information Technology (CIT)

    Please join the Computational Biology Core as we extend the series of the Introduction to R Workshops.  This workshop is 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 .

    We ask that participants have previously attended 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://goo.gl/forms/rfa4N8mzwjaZddwO2

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering
  • Limin Peng, PhD
    Professor, Biostatistics and Bioinformatics
    Rollins School of Public Health
    Emory University

    Title: Quantile Regression Modeling of Latent Trajectory Features with Longitudinal Data

    Quantile regression has demonstrated promising utility in longitudinal data analysis. Existing work is primarily focused on modeling cross-sectional outcomes, while outcome trajectories often carry more substantive information in practice. In this work, we develop a trajectory quantile regression framework that is designed to robustly and flexibly investigate how latent individual trajectory features are related to observed subject characteristics. The proposed models are built under multilevel modeling with usual parametric assumptions lifted or relaxed. We derive our estimation procedure by novelly transforming the problem at hand to quantile regression with perturbed responses and adapting the bias correction technique for handling covariate measurement errors. We establish desirable asymptotic properties of the proposed estimator, including uniform consistency and weak convergence. Extensive simulation studies confirm the validity of the proposed method as well as its robustness. An application to the DURABLE trial uncovers sensible scientific findings and illustrates the practical value of our proposals.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Research, Training, Professional Development
  • Mar
    4
    5:00pm - 6:30pm

    POSTPONED: Team Science Workshop

    Women & Infants Hospital

    Please note: Today’s Team Science Workshop has been postponed due to inclement weather. More information about the rescheduled date and details will be available soon. Please contact [email protected] with any questions.

    Register now for, “Better Together: Putting Team Science Theory into Practice to Enhance Your Research,” a workshop presented by Debbie Cornman, PhD, and Katie Sharkey, MD, PhD. 

    Medical research continues to move away from the traditional “independent investigator” model, and toward collaborative approaches that integrate diverse disciplines with traditional biomedical sciences.

    The National Institutes of Health established team science approaches as a major goal in its 2003 Roadmap . Yet, despite educational efforts and funding incentives, barriers toward achieving this goal remain. Many investigators are not aware of how to implement team science approaches into their research, or utilize available support.

    In this workshop, Debbie Cornman, PhD and Katie Sharkey, MD, PhD will discuss the latest findings in the “science of team science,” and teach investigators how to implement strategies and techniques for successful team science approaches into their own research.

    By the conclusion of the workshop, attendees will:

    • Discuss the theories behind a team science approach
    • Appraise their own readiness for engaging in team science
    • Apply the team science approach to their own research questions

    Who should attend:

    All faculty, researchers, and affiliated staff at Brown, URI, and the affiliated hospital systems who are interested in team-based science approaches. Physician scientists and junior faculty are encouraged to attend.

    Accreditation:

    The Warren Alpert Medical School of Brown University is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

    Credit designation:

    Physicians: The Warren Alpert Medical School of Brown University designates this live activity for a maximum of 1.5 AMA PRA Category 1 Credits™ . Physicians should claim only the credit commensurate with the extent of their participation in the activity.

    Learn about the presenters

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Mar
    5
    12:00pm - 1:30pm

    Advance-CTR Biostatistics Core Seminar Series

    70 Ship Street

    Presented by the Research Design, Epidemiology, and Biostatistics Core of Advance-CTR, this bi-monthly Seminar Series offers instruction on data analysis (first Tuesday of each month), and study design (last Thursday of each month). The Seminars are available to attend in-person, or remotely via Zoom.

    Jess Kaminsky & Gabriella Silva: “Exploratory Data Analysis”

    Types of variables, summary statistics, measures of center, basic graphs (boxplots, histograms, etc).

    Register to watch remotely .

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Mar
    7
    12:00pm - 1:00pm

    Data Science, Computing, and Visualization Workshop

    164 Angell Street

    March 7: Tidying, Transforming, and Visualizing Data in R

    Want to become a Kaggle 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?

    • What: Lunchtime workshops targeting a range of skill levels; food will be served
    • When: Thursdays, 12-1pm
    • Where: Carney Institute Innovation Hub, 164 Angell Street, 4th floor (above the bookstore)
    • Who: Open to all members of the Brown community; led by Brown faculty, staff, and students; limited to 40 participants

    More information and future topics on the DSI website .
    Lunch is provided (pizza).

  • Mar
    8
    9:00am - 4:00pm

    Publishing Python Data Analysis Projects: Workshop

    164 Angell Street

    This workshop is for computational researchers who work in python to learn to organize and package their code for release. After this workshop, learners will be prepared to reorganize and existing project or set up existing projects to be released as open source code that is reusable by others in their work, beyond reproducing a paper’s result.

    See the learner profiles for more details on the target audience.

    Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed here ). They are also required to abide by Code of Conduct .

    This workshop is primarily for researchers with a Brown University affiliation. Others may attend with prior approval from the organizer.

    Advance registration is required. Please register here

    Kabob and Curry will be provided for lunch.

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering
  • Mar
    11
    4:00pm

    Ashley Buchanan - DSI Guest Speaker

    164 Angell Street

    Toward Evaluation of Dissemination of HIV Prevention Interventions Among Networks of People who Inject Drugs

    Like many other populations, people who inject drugs are embedded in social networks or communities (e.g., injection drug, non-injection drug, sexual risk) and exert biological and social influence on the members of their social networks. The direct effect is the effect on the index participants (i.e., participants who received the intervention) and the disseminated (or indirect) effect is the effect on the participants who shared a network with the index participant. We analyzed a network of people who inject drugs from the Social Factors and HIV Risk Study (SFHR), 1991 to 1993, Bushwick, New York, where links were defined by shared sexual and injection behaviors. In our setting, the study design is an observed network with a nonrandomized intervention or exposure, where information is available on each participant and their directional HIV risk connections. We assumed that smaller groupings or neighborhoods for each individual can be identified in the data. We used an inverse probability weighted approach to quantify the direct and disseminated effects of health beliefs on health-seeking behavior. Our results provide evidence of disseminated effects of attitudes among PWIDs networks and will inform the development of more effective network-based interventions that consider attitudes to prevent HIV/AIDS and improve care among PWIDs.

     

    Ashley Buchanan, Assistant Professor

    Department of Pharmacy Practice, College of Pharmacy

    University of Rhode Island

  • Susan Athey, PhD

    The Economics of Technology Professor

    Stanford Graduate School of Business

    “Machine Learning and Causal Inference for Heterogeneous Treatment Effects”

    This talk will review recently developed methods to apply machine learning methods to causal inference problems, including the problems of estimating heterogeneous treatment effects, for example, in A/B testing, as well as in estimating optimal treatment assignment policies.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Mar
    13
    4:00pm

    DSI Colloquia: Priya Kohli

    164 Angell Street

    Parsimonious Data-Based Methods for Covariance Estimation in Multivariate Longitudinal Data

    Multivariate longitudinal studies are quite common in clinical trials and biological research. Studying the joint evolution and dependence patterns among outcomes of subjects over time is of interest in making recommendations for the treatment and follow-up of subjects to guide the development of appropriate interventions. The covariance matrix is a fundamental quantity that helps us understand the evolution and dynamic nature of relationships among multiple outcomes. Estimating the covariance matrix is quite challenging in multivariate longitudinal applications where the number of parameters grows quadratically with both the number of outcomes and repeated time points.

    Few, if any, graphical and data-driven tools have been explored for multivariate longitudinal data. In this work, we tackle this challenge by using modified Cholesky block parameters of the inverse covariance matrix and extending the regressograms to visualize dependence patterns present in the multivariate longitudinal studies. We illustrate the applicability of the proposed approach through empirical examples.

    * Joint work with Tanya P. Garcia and Mohsen Pourahmadi.

  • Mar
    15
    8:00am - 12:00pm

    Systematic Review Half-Day Course

    121 South Main Street

    Presented by the Clinical Research Design, Epidemiology, and Biostatistics Core of Advance-CTR

    What is evidence-based medicine, and how do we sort and evaluate available evidence to support decision-makers?

    In this half-day course, experts from Brown’s School of Public Health will discuss:

    • The role and value of systematic reviews, the basic steps involved in conducting a systematic review, and how they can be applied to public health and health policy;
    • The major principles and techniques of statistical analysis of meta-analytic data with a focus on summary data from reports and individual data from studies; and
    • Meta-analysis in the context of evidence-based science, with discussion around the basic principles of network meta-analysis and the validity of its assumptions, including the key role that potential effect modifiers play.

    Brown University research librarians will also discuss the resources and support available to clinical and academic faculty who would like to conduct a systematic review.

    Participants will gain an increased awareness and knowledge of how systematic reviews can:

    • Enable better choice of or recommendation of treatments; and
    • Lead to more accurate diagnosis.

    Accreditation:

    The Warren Alpert Medical School is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

    Credit Designation:

    • Physicians: The Warren Alpert Medical School of Brown University designates this live activity for a maximum of 3.5 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

    • Other Healthcare Professionals: Participants will receive a Certificate of Attendance stating this program is designated for 3.5 hours AMA PRA Category 1 Credits™. This credit is accepted by the AAPA and AANP.

    Featuring:

    • Kristin Danko, PhD, Postdoctoral Research Associate in Health Services, Policy and Practice at the Brown University School of Public Health
    • Ian Saldanha, MD, MPH, Assistant Professor of Health Services, Policy and Practice, Assistant Professor of Epidemiology at the Brown University School of Public Health
    • Christopher Schmid, PhD, Professor of Biostatistics, Chair of Biostatistics at the Brown University School of Public Health

    Details: This course is free but registration is required as space is limited. Register now using the link above. 

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Mar
    20
    4:00pm - 5:00pm

    COBRE CBHD Seminar: Iuliana Ene, PhD

    164 Angell Street

    Please join the COBRE Center for Computational Biology of Human Disease, the Center for Computational Molecular Biology, and the Data Science Initiatve for the COBRE CBHD Seminar.  

    Iuliana Ene, PhD, MMI Department will present on her pilot project and on her submitted research proposal.

    Title:  Genome evolution and population heterogeneity in a human fungal pathogen

  • Mar
    26
    11:00am - 3:00pm

    22nd Mind Brain Research Day

    Brown Campus

    Event Date:              Tuesday, March 26th

    Keynote Address: Begins promptly at 1:00 pm, Salomon Hall

    Keynote Speaker:  Joshua A. Gordon, MD, PhD

                                     Director, National Institute of Mental Health

    Talk Title:                 On Being a Circuit Psychiatrist

    Poster Session: 11:00 am—12:45 pm

    Lunch: Available from 11:30 am—12:45 pm at Sayles Hall

    YOU MUST RSVP TO RESERVE A LUNCH (even if you submitted a poster)

                                                     PLEASE RSVP HERE

     Poster Awards: After keynote address

  • Mar
    28
    12:00pm - 12:45pm

    Meet the Mindfulness Center: Open Community Meditation

    South Street Landing

    The Mindfulness Center at Brown welcomes you to a lunchtime mindfulness practice. Join us for a guided sitting meditation and discussion on mindfulness.

    Free and open to the public.

    Thursday, March 28, 2019, 12:00–12:45pm.

    South Street Landing , Room 498

    Please RSVP here .

    About the Mindfulness Center at Brown: Mindfulness Research, Education and Training

    The Mindfulness Center at Brown University has educational programs for newcomers and seasoned practitioners.

    The Mindfulness Center at Brown brings together top academics in mindfulness research with leading educators in the field.

    We are dedicated to rigorous research, student-centered education, and a global presence to share mindfulness practices that improve individual lives and organizational effectiveness.

    http://www.brown.edu/mindfulnesscenter

    Athletics, Sports, Wellness, Community Meditation, Free, Health, Meditation, Mindfulness
  • Mar
    28
    12:00pm - 1:30pm

    Advance-CTR Biostatistics Core Seminar Series

    Brown University Medical Education Building (Alpert Medical School)

    Presented by the Research Design, Epidemiology, and Biostatistics Core of Advance-CTR, this bi-monthly Seminar Series offers instruction on data analysis (first Tuesday of each month), and study design (last Thursday of each month). The Seminars are available to attend in-person, or remotely via Zoom.

    Jess Kaminsky & Gabriella Silva: “Experimental Study Design”

    Advantages and disadvantages of experimental studies, individual vs. group trials, preventive vs. therapeutic trials, parallel RCTs, factorial study design, new drug trials, sources of potential bias.

    Register to watch remotely .

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • The Carney Institute is launching a new monthly series to share opportunities as well as some of the incredible work going on around the institute.
    The first Carney Institute Coffee Hour will be held March 28, 3 - 4 p.m., in our Innovation Zone. If you’re a graduate student or postdoc in brain science, join Carney Institute Director Diane Lipscombe and Managing Director John Davenport for a discussion of current funding trends and career advice.
  • Apr
    1

    Computational neuroscience for causal tools in humans

    Mitsuo Kawato

    Advanced Telecommunications Research Institute International

    Kyoto, Japan

     

    Hosts: Takeo Watanabe, Yuka Sasaki, and Jerome Sanes

  • Nilanjan Chatterjee, PhD
    Bloomberg Distinguished Professor, Department of Biostatistics, Bloomberg School of Public Health
    Department of Oncology, School of Medicine
    Johns Hopkins University

    Leveraging Polygenicity of Complex Traits to Inform Biology, Causality and Prediction”

    Using results from modern genome-wide association studies (GWAS), we and others have now unequivocally demonstrated that complex traits are extremely polygenic, with each individual trait potentially involving thousands to tens of thousands of genetic variants. While each individual variant may have a small effect on a given trait, in combination, they can explain substantial variation of the trait in the underlying population. In the past, analyses of GWAS have mainly focused on modelling genetic susceptibility one-variant-at-a-time, and identifying those which reach stringent statistical significance for association. In the future, however, we advocate that analysis needs to focus more on polygenic modelling to exploit the power of diffused signals in GWAS. In this talk, I will review recent advances in statistical methods for polygenic analysis, as well as scientific knowledge gained through their applications, in three areas of major interest (i) understanding biology through genomic enrichment analysis (ii) exploring causality through Mendelian Randomization (Genetic Instrumental Variable) analysis and (iii) and informing precision medicine through development of risk prediction models.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Apr
    2
    12:00pm - 1:30pm

    Advance-CTR Biostatistics Core Seminar Series

    70 Ship Street

    Presented by the Research Design, Epidemiology, and Biostatistics Core of Advance-CTR, this bi-monthly Seminar Series offers instruction on data analysis (first Tuesday of each month), and study design (last Thursday of each month). The Seminars are available to attend in-person, or remotely via Zoom.

    Jess Kaminsky & Gabriella Silva: “Introduction to Probability”

    Language of probability, basic set theory, basic conditional probability and bayes’ theorem, independence, basics of diagnostic test statistics.

    Register to watch remotely .

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Apr
    3
    2:00pm - 5:00pm

    Clinical Research Office Hours

    URI Nursing Education Center

    Clinical Research Office Hours are now available in Providence!

    If you have questions about a clinical research project, don’t miss this opportunity to speak directly with our experts, Bharat Ramratnam, MD, chief science officer and medical director at the Lifespan Clinical Research Center; and Andrew Schumacher, MSCHE, director of clinical trials for the Lifespan Oncology Clinical Research Office and the Lifespan Clinical Research Center.

    Expertise Includes:

    • Clinical trials operations
    • Study design
    • Budget development
    • Regulatory proposals

    Who Should Attend:

    Rhode Island investigators and research staff are encouraged to attend these office hours to discuss any questions as they relate to clinical research and/or specific projects.

    Event Details and Registration:

    Office Hours will be held on Wednesday, April 3, 2019 from 2-5 p.m. at the URI Nursing Education Center, 350 Eddy Street, Room 224, in Providence, Rhode Island. Attendees must register in advance to reserve a time slot with our experts. Availability for walk-ins cannot be guaranteed. Sign up now .

    Advising, Mentorship, Biology, Medicine, Public Health, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Psychology & Cognitive Sciences, Research, Social Sciences, Training, Professional Development
  • Apr
    3
    4:00pm - 5:00pm

    COBRE CBHD Seminar: Lorin Crawford, PhD

    164 Angell Street

    Please join the COBRE Center for Computational Biology of Human Disease (CBHD), the Center for Computational Molecular Biology, and the Data Science Initiative for the COBRE CBHD Seminar.

    Lorin Crawford, PhD, from the Dept of Biostatisticis in the School of Public Health will present on his recently ended COBRE CBHD Institutional pilot award and his new COBRE CBHD research project.  His talk is titled, “Machine Learning Methods for Epistatic Mapping and Discovery in Genome-wide Association Studies.”

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Research
  • How can a social analytics company help world class brands like Amazon, Warner Brothers, or Michael Kors understand their customers better? Join Brown alum co-founders and co-CEOs, Christian Anthony ’96 and Jason Klein ’00, and learn how they built ListenFirst, an industry leader in social analytics for major consumer brands and the only social analytics company that provides the full spectrum of online engagement for their customers.

    With deep roots in media & entertainment, ListenFirst takes social analytics and connects to business outcomes. Their comprehensive social intelligence solutions analyze and unite billions of real-time digital and social media signals across more than 50,000 brands and 20+ industries — from owned, earned, paid, conversation, and video content engagement to behavioral analytics, brand affinities, and influencer tracking. This information empowers brands to develop engaging content, set KPIs, optimize strategy and performance, and drive their bottom line. Built for marketers by marketers, ListenFirst was founded to drive a future where data, not just instinct, helps guide the way brands make decisions.

    Meet the speakers:

    Christian is a serial entrepreneur with extensive experience in business strategy, finance, marketing and advertising. After beginning his career as an investment banker at JP Morgan, he founded InSound, an online music store, in 1998. Four years later, he founded Special Ops Media, a first-of-its-kind digital agency that worked with top global brands such as The Coca-Cola Company, Dell, PUMA, North America and Boots Healthcare. After Special Ops was acquired by LBi in 2008, he led the company as CEO before stepping down to start ListenFirst. Christian graduated from Brown University. He serves on Brown University’s President’s Advisory Council on Entrepreneurship and is a Board Member at Venture for America, as well as a member of Young President’s Organization.

    Jason is a multi-time entrepreneur who has helped brands navigate the changing digital landscape for decades. He founded Special Ops Media (acquired by LBi in 2008) and then served as Co-President of LBi US (later acquired by Publicis in 2012). At LBi, he was responsible for the agency’s Media, ePR and Social Media offerings. Jason has appeared on countless industry lists, including Variety’s Indie 50, MediaPost’s Rising Stars of Digital Media, and, most recently, Ad Age’s prestigious 40 Under 40. Jason graduated Magna Cum Laude from Brown University and attended medical school at Columbia University. He serves on the Board of Scenarios USA.

    RSVP here.  All of our events are open to the public.

    A bagged lunch will be served following the event. 

  • Abstract

    Native Americans have higher rates than whites of contracting, experiencing complications from, and death due to diabetes and other diseases. As stress may exacerbate the effects of many diseases in Native communities, research is beginning to explore how stress reduction interventions may affect long-term health in these communities. However, psychological stress in these communities needs to be understood within the complex intersection of daily stress compounded by historical stress. Further, a distrust of western medicine in Native communities and poor understanding in psychology and medicine of Native communities is often an obstacle to effective communication and treatment.

    Dr. Proulx’s presentation explores the development of mindfulness-based programs that were designed to address diabetes in Native communities in Oregon and California. His approach addresses the intersectionality of cultural trauma, daily stress, and poor health behaviors with the hope that 8 weeks of practicing the Native mindfulness class will reduce the physiologic risk that stress contributes to diabetes outcomes as well as reduce the psychological stress of managing diabetes. In particular, he explores the long process of trust building and community engagement that allowed us to gain community buy-in and commitment to the mindfulness programs. Further, he discusses how community members were invited to guide the years-long research study and adapt the current models of stress reduction and mindfulness to include Native traditions that are also inherently mindful. He will also discuss the challenges of engaging in psychosocial and medical research in rural Native communities and will provide opportunity for discussion on these topics.

     

    About Dr. Jeffrey Proulx

    Dr. Jeffrey Proulx’s primary research focuses on developmental health psychology and integrative health. He is particularly interested in the relationship between psychological stress and long-term physical health. Further, he is interested in the development of stress reduction programs, particularly those that are contemplative-based, as a means to protect health across the lifespan. He is a Native American with a strong interest in the study of health in underserved communities and much of his recent work has centered on culturally-specific stress reduction programs. To this end, he is currently funded by a K99/R00 award from the National Institutes of Health (NIH) National Center for Complementary and Integrative Health to study the effects of mindfulness in Native American communities as a means to address diabetes. His professional activities include participation on the OHSU School of Medicine Wellness Committee (with a focus on physician/nurse burnout) and he serves on the Spiritual Care Team for patients at the hospital. He is also currently working with researchers at Oregon State University on research exploring the effects of meditation in Native communities in Oregon and on other stress and coping-related projects. All of this is tied into his long-term effort to understand the extent to which integrative stress reduction methods can lower stress-related diseases among individuals and the practitioners who care for them.

    Biology, Medicine, Public Health, Free, Health Equity, Health, Identity, Culture, Inclusion, Mindfulness, Native American, Research, Social Sciences
  • DSCoV lunch and workshop lead by Mike X. Cohen, Associate professor at Radboud University Medical Centre, Nijmegen, Netherlands, and Donders Centre for Neuroscience Event abstract: Increases in the number of simultaneously recorded electrodes allows new discoveries about the spatiotemporal structure in the brain, but also presents new challenges for data analyses. “Source separation” analyses generally have the goal of taking weighted combinations of electrodes to obtain a single component, thus reducing the number of dimensions from M electrodes to C components. The workshop will introduce one family of source separation techniques, which is based on generalized eigendecomposition, and will include a mix of lecture/discussion and hands-on practical work in MATLAB using simulated and real electrophysiology data. Bring your laptop! Pizza will be served. Space is limited – please RSVP through the link below: https://www.eventbrite.com/e/dscov-lunch-nonorthogonal-source-separation-for-guided-network-discovery-tickets-59870242507?ref=estw

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research
  • Apr
    15
    4:00pm

    DSI Colloquium: Jeremy Kepner

    164 Angell Street
    Enabling AI Impact
    Recent AI miracles in vision, language, and strategy are the result of enormous strides in computing, big data, and algorithms. Effectively exploiting these AI capabilities for science and engineering requires mathematically rigorous interfaces that allow scientists and engineers to focus on their research and avoid rewriting software. Mathematically rigorous interfaces are at the core of the MIT SuperCloud and let it deliver leading-edge technologies to thousands of scientists and engineers. This talk discusses the rapidly evolving AI computing landscape and how mathematically rigorous interfaces are key to exploiting advanced computing capabilities.
    Dr. Kepner is a Lincoln Laboratory Fellow and founder of the MIT Lincoln Laboratory Supercomputing Center (LLSC). He is focused on creating state-of-the-art computing capabilities to enable scientific and engineering breakthroughs to solve the Nation’s most challenging problems. Dr. Kepner co-invented the Massachusetts Green High Performance Supercomputing Center (MGHPCC), the largest and “greenest” open research data center in the world that has enabled a dramatic increase in MIT’s computing capabilities while reducing its CO2 footprint. The LLSC’s pioneering interactive supercomputing capability and unique MIT SuperCloud system accelerates the award-winning innovations of thousands of MIT researchers. Under Dr. Kepner’s leadership, the LLSC team has been recognized for its outstanding contributions to numerous R&D 100 Award-winning technologies that have improved airline safety, prevented the spread of new diseases, and aided numerous hurricane and earthquake victims. Dr. Kepner is the founding sponsor of the Wilkinson Prize-winning Julia programming language used by millions of programmers world-wide.
    Host: Andrew Crotty
  • Leonidas Bantis, PhD

    Assistant Professor

    Department of Biostatistics

    University of Kansas Medical Center

    Inference in ROC curves for two phase nested case-control biomarker studies”

    Two-phase nested case-control sampling designs are common in biomarker evaluation studies. In the first phase (phase I), a large sample size that is representative of the target population is available and is referenced mainly for clinical characteristics of the participants. In the second phase (phase II), biomarker measurements are taken only on a subsample of phase I participants due to limited resources. Since this subsampling is usually done based on some matching criteria, inherent bias is present. For example, when dealing with a lung cancer study, matching cases and controls for smoking status may be crucial. This biased sampling needs to be taken into account when clinical questions that refer to the target population are of interest. While many researchers focus on the area under the ROC curve to assess the discriminatory ability of a biomarker, such a measure does not provide an appealing clinical interpretation. Clinicians are most often interested in the performance of a biomarker at high levels of sensitivity or specificity to avoid under-diagnosis or over-diagnosis, respectively. This is driven by the seriousness of a false negative or the invasiveness of the work-up required to identify a false positive. We develop a new statistical methodology to obtain estimates of the sensitivity at a given specificity (or vice versa), i.e. for the ROC(t), at a given t, with its corresponding confidence intervals, while also accounting for the aforementioned biased sampling scheme. We provide estimates for the ROC curve that are parametric, flexible parametric based on power transformations, and smooth kernel-based non-parametric. We also explore the construction of the corresponding confidence intervals. We prove asymptotic consistency of our estimators and provide an easy to use optimal bandwidth in terms of asymptotic mean integrated squared error. We will discuss two real data sets related to lung and prostate cancer.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Training, Professional Development
  • Apr
    17
    12:00pm - 2:00pm

    MNE-Python EEG Data Analysis Workshop Series: Day 2

    164 Angell Street, 4th floor
    This is the second of a series of three workshops on EEG data analysis using MNE-python (https://martinos.org/mne/stable/index.html ). See https://jasmainak.github.io/mne-workshop-brown/intro for further details.
    Please complete this form to confirm your registration for Day 2 of the EEG Data Analysis Workshop (details on the workshop can be found below). Attendees of Day 1 will be given first access to the registration, but please note that registration will be open to the general public on April 12, and the space is still limited to 40 participants.

    During Day 1 (April 5th), we discussed methods for going from raw data to epoch averaged responses. A video capture of Day 1 can be found here: https://www.dropbox.com/s/x0elvmo6785o7tb/20190405_mne_workshop_day1.mp4?dl=0

    Day 2 (April 17) will focus on data preprocessing using different filtering methods, including temporal filtering, Autoreect for artifact rejection, and spatial filtering.

    Day 3 (date TBD) will focus on source localization methods.
     
    The tutorial will be accessed through a Hub, as such a valid email address is required by all participants. If you have one, please use your Brown University email address. The workshop is limited to 40 participants., and we will check to ensure that the attendee information matches the information in the form above.
    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research
  • Apr
    17
    4:00pm - 5:30pm

    CCMB Honors Thesis presentations

    164 Angell Street
  • LCDS and DSI Seminar

    Thursday, April 18, 2019
    4:00pm
    Room 108, 170 Hope Street

    Sherry Towers, Arizona State University

     

    “Assessing the average length of infection chains in outbreaks of infectious disease like measles”

    Analytical expressions for the reproduction number, R0, and final size have been obtained in the past for an extremely wide variety of mathematical models for infectious disease spread. However, what has so far not been studied is the average number of infections that descend down the chains of infection begun by each of the individuals infected in an outbreak (we refer to this quantity as the “average number of descendant infections” per infectious individual, or ANDI). ANDI includes not only the number of people that an individual directly contacts and infects, but also the number of people that those go on to infect, and so on until the chain of infection dies out. Quantification of ANDI has relevance to the anti-vaccine debate; if the individual probability of hospitalization with a disease is p, the probability that at least one individual down an average chain of infection is hospitalized is 1-(1-p)^ANDI.
    Here we examine a Susceptible, Infected, Recovered (SIR) model, and obtain the analytic expression for ANDI. We compare the estimates of ANDI from this expression to those obtained using an Agent Base Monte Carlo formalism that keeps track of who infected whom. We find that ANDI is maximized for smaller effective R0, and that ANDI in even relatively small populations can be literally dozens of individuals. For outbreaks of diseases like measles in populations with sub-standard vaccine coverage, we find that the infection of an unvaccinated individual on average has almost 100% probability of resulting in the hospitalization of at least one person down the infection chain that began with that individual.
    While the model examined in this initial analysis is simple, the analytic and computational formalisms we have developed can be expanded to a wide variety of models for disease spread.
  • Eloise Kaizar, PhD

    Associate Professor of Statistics

    Co-Vice Chair for Undergraduate Studies and Administration

    Ohio State University

    “Watching your Weights: Generalizing from a Randomized Trial to a ‘Real World’ Target Population”

    Randomized controlled trials are often thought to provide definitive evidence on the magnitude of treatment effects. But because treatment modifiers may have a different distribution in a real world population than among trial participants, trial results may not directly reflect the average treatment effect that would follow real world adoption of a new treatment. Recently, weight-based methods have been repurposed to more provide more relevant average effect estimates for real populations. In this talk, I summarize important analytical choices involving what should and should not be borrowed from other applications of weight-based estimators, make evidence-based recommendations about confidence interval construction, and present conjectures about best choices for other aspects of statistical inference.

    Biology, Medicine, Public Health, BioStatsSeminar, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Are you curious about the five Carney Institute centers and initiatives?

    Over coffee and baked goods, get an insider’s look at the centers and discover what they offer to the Carney community – from grant opportunities to fellowships to workshops.

    Carney Institute Centers & Initiatives

    Computation in Brain and Mind

    Center for Vision Research

    COBRE Center for Central Nervous System Function

    MRI Research Facility

    Center for the Neurobiology of Cells and Circuits

  • Flavio du Pin Calmon
    Apr
    24
    4:00pm

    DSI Colloquium: Flavio du Pin Calmon

    164 Angell Street

    FLAVIO DU PIN CALMON

    Assistant Professor of Electrical Engineering

    John A. Paulson School of Engineering, Harvard University

     

    On Representations and Fairness: A Few Information-Theoretic Tools for Machine Learning

    Information theory can shed light on the algorithm-independent limits of learning from data and serve as a design driver for new machine learning algorithms. In this talk, we discuss a set of information-theoretic tools that can be used to (i) help understand fairness and discrimination in machine learning and (ii) characterize data representations learned by complex learning models. On the fairness side, we explore how local perturbations of distributions can help both identify proxy features for discrimination as well as repair models for bias. On the representation learning side, we explore a theoretical tool called principal inertia components (PICs), which enjoy a long history in the statistics and information theory literature. We use the PICs to scale-up a multivariate statistical tool called correspondence analysis (CA) using neural networks, enabling data dependencies to be visualized and interpreted at a large scale. We illustrate these techniques in both synthetic and real-world datasets and discuss future research directions.

     

    Flavio P. Calmon is an Assistant Professor of Electrical Engineering at Harvard’s John A. Paulson School of Engineering and Applied Sciences . Before joining Harvard, he was the inaugural data science for social good post-doctoral fellow at IBM Research in Yorktown Heights, New York. He received his Ph.D. in Electrical Engineering and Computer Science at MIT. His main research interests are information theory, inference, and statistics, with applications to fairness, privacy, machine learning, and communications engineering. Prof. Calmon has received the NSF CAREER award, the Google Research Faculty Award, the IBM Open Collaborative Research Award, and Harvard’s Lemann Brazil Research Fund Award.

     

    Wednesday, April 24

    4:00 PM – 5:00 PM

    164 Angell Street, 3rd Floor Open Space

     

    Coffee and pastry to be served.

    Hosted by Sarah M. Brown.

    Sponsored by the Data Science Initiative.

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Research
  • Apr
    25
    12:00pm - 1:00pm

    DSCoV Workshop

    164 Angell Street

    Data Science, Computing, and Visualization workshops are held weekly on Thursdays at noon. These are workshops on basic and advanced data science skills–different topics each week, targeting a range of skill levels. For more information, upcoming topics, and registration see the DSI website.  Lunch is served (usually pizza).

  • Apr
    25

    In this talk I will survey our work on understanding and improving technology’s role in intimate partner violence (IPV). IPV is a widespread social ill affecting one in four women and one in six men at some point in their lives. In interviews with survivors and the professionals that work with them in New York City, our research has provided the most granular view to date of technology abuse. Abusers install spyware on mobile devices, compromise victim accounts, exploit social media for harassment, and much more. We complemented this qualitative work with a first-of-its-kind measurement study that discovered a large ecosystem of online resources aimed at helping abusers, including a variety of apps usable as IPV spyware.

    Unfortunately, abusers require little technology expertise to mount devastating attacks. Instead, the context of IPV undermines the assumptions underlying traditional security mechanisms such as passwords and malware detection, and, more broadly, we believe traditional approaches to computer security are not yet up to the task of helping IPV victims. We have therefore initiated work on building up a theory and practice of clinical computer security, in which trained technology professionals meet with victims to help diagnose digital insecurities and advise on potential remediations. I will discuss our ongoing field work prototyping clinical services, including deployment of a first-of-its-kind spyware detection tool in New York City, and conclude with a discussion of other domains where clinical computer security may prove useful.

    This is covering joint work with: Rahul Chatterjee, Nicola Dell, Peri Doerfler, Sam Havron, Karen Levy, Damon McCoy, Diana Minchala, Hadas Orgad, and Jackeline Palmer

    Hosts: Professor Roberto Tamassia and Evgenios Kornaropoulos

  • Apr
    25
    3:00pm - 7:00pm

    Inclusion and Representation in the Sciences

    164 Angell Street

    This event will showcase three student-driven courses at Amherst, Brown, and Yale that focus on inclusion and representation in the sciences and provide an opportunity to engage in discussions about the content and impact of these courses. The “Being Human in Stem” Initiative at Amherst and Yale aims to foster a more inclusive, supportive STEM community by helping students, faculty, and staff collaboratively develop a framework to understand and navigate diverse identities in the classroom, lab, and beyond. The Brown course on “Race and Gender in the Scientific Community” examines disparities in representation in the scientific community, issues facing different groups in the sciences, and paths towards a more inclusive scientific environment.

    Please see schedule and register here .

    Keynote address takes place in Foxboro Auditorium, Kassar House, 151 Thayer St.

    Identity, Culture, Inclusion, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences
  • Apr
    26
    8:30am - 6:00pm

    3rd NorthEast Computational Health Summit (NECHS) 2019

    South Street Landing

    On Friday April 26th, Brown University and IBM Research are co-hosting the 3rd NorthEast Computational Health Summit (NECHS) focused on the latest developments in Personal and Population Health in the Digital Era. The goal of the summit is to foster communication and collaboration among clinicians, medical informaticists, and computational researchers in the northeast.

    The summit will feature two invited keynotes:

    • Susan Murphy, Professor of Statistics, Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University
    • Peter Embi, President & CEO Regenstrief Institute; Associate Dean for Informatics & Health Services Research, Indiana University School of Medicine

    There will also be an invited panel presentation led by Benjamin Marlin of the University of Massachusetts, Amherst as well as submitted papers and posters on various aspects of digital health including deep learning, observational studies, predictive modelling, translational informatics, patient engagement and health behavior.

    There will be ample time for less formal interactions during lunch, as well as during the poster session and breaks.

    To register for this event, please visit  https://nechs2019.eventbrite.com .

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Research
  • Ali Momeni

    250th Anniversary Professor of the Practice

    Visiting Fellow in Music

     

    Ali Momeni is a Professor of Practice within the Brown Arts Initiative and Data Science Initiative . His research interests include educational technologies, human-computer interaction for performative applications of robotics, playful urban interventions, interactive projection performance, machine learning for artists and designers, interactive tools or storytelling and experiential learning, mobile and hybrid musical instruments, and the intersection of sound, music and health.

  • May
    8
    4:00pm - 5:00pm

    COBRE CBHD Seminar: Marco De Cecco, PhD

    164 Angell Street

    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.  

    Marco De Cecco will present.

    Title: TBD

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research
  • May
    8
    4:00pm - 5:00pm

    COBRE CBHD Seminar: Marco De Cecco, PhD

    164 Angell Street

    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.  

    Marco De Cecco will present on his newly funded COBRE CBHD research project “The Role of Line-1 Senescence-Associated Cytoplasmic DNA Accumulation.”

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research
  • Forrest Crawford, PhD
    May
    13
    4:00pm

    Forrest Crawford, PhD

    164 Angell Street

    FORREST CRAWFORD

    Associate Professor, Department of Biostatistics, Operations, and Ecology, and Evolutionary Biology, School of Public Health, Yale University

     

    Causal Inference Under Spillover and Contagion:  Structural Versus Agnostic Methods

    How do researchers learn about the effects of interventions when treatment to one individual affects others? Two competing paradigms dominate statistical approaches toestimating the effects of interventions ininterconnected/interacting groups under spillover or interference between experimental units. “Mechanistic” or “structural” models capture dynamic featuresof the process by which outcomes are generated, permitting inferences with real-worldinterpretations and detailed predictions. “Agnostic”, “design-based”, or “reduced form” approaches, often based on notions of randomization, refrain from specifying thefull joint distribution of the data, and provide inferences that arerobust to model mis-specification. Statisticians, data scientists, economists, epidemiologists, and other scientists often disagree about whichof these paradigms is superior for studies of interventions among potentially interacting individuals, with competing claims about modelrealism, bias, and credibility of inferences. In this presentation, Ireview methods for estimating the causal effect of an individualistic treatment under spillover, with special attention to the case of contagion, whereby units can transmit their outcome to others in a way that depends on their treatment. I define a formal structural model of contagion,and ask what causal features agnostic or reduced-form estimatesrecover. I exhibit analytically and by simulation the circumstancesunder which “agnostic” estimates imply aneffect whose direction is opposite that of the true individualistictreatment effect. Furthermore, I show that widely recommended randomization designs and estimators may provide misleading inferences about the direct effect of an intervention when outcomes are contagious. These ideas are illustrated in three empirical examples: transmission of tuberculosis, product adoption, and peer recruitment in social networks.

     

    Forrest W. Crawford PhD is Associate Professor, Department of Biostatistics, Yale School of Public Health, Yale School of Management (Operations), and Department of Ecology & Evolutionary Biology, Yale University. He is affiliated with the Center for Interdisciplinary Research on AIDS, the Institute for Network Science, the Computational Biology and Bioinformatics program, and the Public Health Modeling concentration. He is the recipient of the NIH Director’s New Innovator Award and a Yale Center for Clinical Investigation Scholar Award. His research interests include causal inference, networks, graphs, stochastic processes, and optimization for applications in epidemiology, public health, and social science.

     

    Monday, May 13

    4:00 PM – 5:00 PM

    164 Angell Street, 3rd Floor Open Space

    Coffee and pastry to be served.

    Sponsored by: Data Science Initiative

  • May
    13
    5:00pm - 6:30pm

    Team Science Workshop

    Women & Infants Hospital

    In this interactive workshop, Katie Sharkey, MD, PhD, and Debbie Cornman, PhD, will discuss the latest findings in the “science of team science,” and teach investigators how to implement strategies and techniques for successful team science approaches into their own research.

    Workshop is free but registration is required. Learn more .

    Advising, Mentorship, Biology, Medicine, Public Health, Careers, Recruiting, Internships, Education, Teaching, Instruction, Entrepreneurship, Graduate School, Postgraduate Education, Research, Training, Professional Development
  • Is there inherent beauty deeply embedded in our brain? Can we visualize the mind? How noisy sensory data measured from scalp EEG can help us understand the orchestra of the human mind? Presenter: Prasanta Pal Pizza will be served. Seats are limited. Please register.

    Biology, Medicine, Public Health, Faith, Spirituality, Worship, Mathematics, Technology, Engineering, Philosophy, Religious Studies, Physical & Earth Sciences, Psychology & Cognitive Sciences, Research
  • May
    26
    All Day

    Commencement

    Commencement

    Academic Calendar, University Dates & Events, Spring Semester
  • May
    30
    12:00pm

    DSCoV Workshop: Paul Stey

    164 Angell Street
    Want to become a Kaggle 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?
    • What: Lunchtime workshops targeting a range of skill levels; food will be served
    • When: Thursdays, 12 PM - 1 PM
    • Where: Carney Institute Innovation Hub, 164 Angell Street, 4th floor (above the bookstore)
    • Who: Open to all members of the Brown community; led by Brown faculty, staff, and students; limited to 40 participants



    This Week’s Topic:

    Please join us this Thursday, May 30 at noon in the Carney Innovation Hub for Solving the Two-Language Problem with Julia.

    The Julia language aims to give its users the rare combination of exceptional performance and expressiveness. its syntax is similar to MATLAB or Python, but its performance is on the order of C, Fortran, or C++. This tutorial provides an introduction to Julia, explores its many features, and demonstrates its advantages for data science and technical computing more broadly. No prior experience with Julia is necessary.

    This workshop will be instructed by Paul Stey, Director of Computing and Data Science at Brown. Pizza will be served. Please use the link below to see further event information and RSVP, as seats are limited.

    Please register here.
    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Research
  • Jun
    6
    12:00pm

    DSCoV Workshop: Ben Knorlein

    164 Angell Street

    Ben Knorlein, CCV

    “Getting Started with Virtual Reality”

    This workshop will provide an introduction to Virtual Reality and how it can easily be applied to research. We will demonstrate 3 programs that can be used to visualize and interact with data in VR: Paraview (scientific visualization), A-Frame (web visualization), and Unity3D (game development platform).

    Registration is required. Please use this link

     

    164 Angell Street, 4th Floor

    Carney Innovation Space

    12:00 pm, lunch will be served

    Organized by Thomas Serre.

    Sponsored by the Data Science Initiative.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Social Sciences
  • Jun
    7
    8:00am - 6:00pm

    Rhode Island IDeA Symposium

    The Warren Alpert Medical School

    Rhode Island’s annual IDeA Symposium brings together research investigators from across the state who are funded by Institutional Development Awards (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health.

    This gathering will showcase research discovery, allow sharing of best practices and an opportunity to better coordinate the basic, clinical, and translational research and investigator development programs that are supported by the IDeA funding programs in Rhode Island.

    Keynote: 

    Andrew Marshall, PhD, Chief Editor of Nature Biotechnology.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Jun
    20
    12:00pm

    DSCoV Workshop - Introduction to Docker

    164 Angell Street

    Docker is a platform to aid in the development and deployment of applications. In this workshop, we will explore Docker and its common use-cases. No knowledge of Docker is required. Familiarity with programming and/or Linux is helpful. Please come with Docker Desktop installed.

    https://www.docker.com/products/docker-desktop

    Presenter: Bradford Roarr, CIS

    Pizza will be served. Seats are limited.

     

    https://www.eventbrite.com/e/dscov-lunch-introduction-to-docker-tickets-63303537585

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Mathematics, Technology, Engineering
  • On June 26, The Carney Institute will be live screening the Society for Neuroscience’s virtual conference: Machine Learning in Neuroscience — Fundamentals and Possibilities . Please join us in the Innovation Zone.

    No need to register for the event through SfN, but please RSVP here so we know how much food to order. Thanks! 

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Research
  • The Center for Computation and Visualization (CCV) is offering an introductory workshop on the Python programming language and natural language processing, targeted toward graduate students and postdocs in the humanities and social sciences. Python is a versatile computer programming language, useful in many contexts including analyzing large bodies of text. No experience with coding is required. Faculty and staff members can register if space is available. In the first week of the workshop, we will learn how to manipulate and plot data, and modify text. In the second week, we will use natural language processing to analyze large sets of text data. NLP allows us to extract topics, analyze sentiment or summarize text that would take too long for humans to read. The workshop runs July 1-12. Each day will consist of a morning lecture (9am-noon), lunch, and hands-on exercises (1pm-4pm). Participants will have the opportunity to work on their own data-intensive projects with help from CCV data scientists during the second week. Lunch and coffee/tea are provided. The workshop is supported by the Data Science Initiative’s NSF-TRIPODS grant.

    Mathematics, Technology, Engineering, Training, Professional Development
  • Stanford PRISM Flyer

     

    Stanford PRISM

    PRISM (Postdoctoral Recruitment Initiative in Sciences and Medicine) is an opportunity for select late-stage graduate students from diverse backgrounds to come to Stanford for a recruitment weekend, interview with potential mentors, and get the inside scoop on postdoctoral training at Stanford. The purpose of this program is to encourage those who might not currently consider a postdoctoral position at Stanford to get a first-hand look at whether Stanford might be a good fit for them. Our goal is to match excellent trainees to excellent mentors at Stanford.
    This opportunity is open to all. We encourage those from backgrounds underrepresented in the sciences to apply, including but not limited to: African Americans, Latinos, Native Americans, Pacific Islanders, Filipinos, those with disabilities or from disadvantaged backgrounds, and those underrepresented on the basis of gender identity or expression or sexual orientation. Applications consist of an application form, a CV, a research statement, a recommendation from your graduate advisor, and the names of up to 6 potential mentors at Stanford.
    For more information or to apply, go to http://postdocs.stanford.edu/PRISM
    PRISM Fall 2019 dates: October 9-12, 2019
    Deadline: July 8, 2019
  • Jul
    11
    8:30am - 4:30pm

    COBRE on Opioids and Overdose Symposium

    Hotel Providence

    Join the COBRE for Opioids and Overdose for this annual symposium on July 11 from 8 a.m. - 4:30 p.m. at the Hotel Providence. The symposium includes a panel discussion with experts and policy makers, as well as research presentations and networking.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Jul
    15

    Brown’s IMSD Program will offer a 3-part training module on “Designing & Delivering Scientific Presentations ” on July 15, 16, and 17 (M,W,F) from 1:00pm- 3:00pm in TBD. Gain insight and practice in effective oral communication of scientific results. Learn how to frame and structure a poster or oral presentation to communicate with your intended audience. Utilize visual materials to highlight key concepts and findings. Gain practice in creating and delivering a brief scientific presentation, responding to questions, interacting with an audience, giving and receiving formative feedback. Instructor: Bjorn Sanstede. Senior Scholar: Jazlyn Nketia. Advanced registration required.

  • Jul
    16
    12:00pm - 1:00pm

    Research Misconduct Educational Workshop

    Horace Mann House

    Interested in learning about what constitutes research misconduct and Brown’s related policy and procedures? Join Keri Godin, Director of the Office of Research Integrity, in this case-based discussion to learn what research misconduct is (and what it isn’t) and how to report an allegation of research misconduct at Brown.

    This session will also focus on recent enforcement actions and identifying ‘ red flags ’ and tools for detection and prevention. Light snacks will be provided, please feel free to bring your lunch.

    Space is limited | RSVP required

    Education, Teaching, Instruction, IRB, ORI, OVPR, Research, Training, Professional Development
  • Jul
    16

    Brown’s IMSD Program will offer a 3-part training module on “Designing & Delivering Scientific Presentations ” on July 15, 16, and 17 (M,W,F) from 1:00pm- 3:00pm in TBD. Gain insight and practice in effective oral communication of scientific results. Learn how to frame and structure a poster or oral presentation to communicate with your intended audience. Utilize visual materials to highlight key concepts and findings. Gain practice in creating and delivering a brief scientific presentation, responding to questions, interacting with an audience, giving and receiving formative feedback. Instructor: Bjorn Sanstede. Senior Scholar: Jazlyn Nketia. Advanced registration required. 

  • Jul
    17
    1:00pm - 2:30pm

    REDCap Workshop: “Getting Started with Creating Databases”

    Rhode Island Hospital Grads Dorm 593 Eddy Street Providence RI

    Hone your REDCap skills in this upcoming workshop, “Getting Started with Creating Databases,” presented by Advance-CTR. 

    The workshop offers an introduction to building databases in REDCap and covers basic concepts and best practices for building a database for your clinical research question. This workshop will be helpful to anyone building a new project in REDCap, and to those who have been tasked with managing or maintaining an existing database. Time at the end of class will be dedicated to individual questions. Requisites: Attendees must watch three introductory videos before class.

    Registration is required as space is limited.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Jul
    17

    Brown’s IMSD Program will offer a 3-part training module on “Designing & Delivering Scientific Presentations ” on July 15, 16, and 17 (M,W,F) from 1:00pm- 3:00pm in TBD. Gain insight and practice in effective oral communication of scientific results. Learn how to frame and structure a poster or oral presentation to communicate with your intended audience. Utilize visual materials to highlight key concepts and findings. Gain practice in creating and delivering a brief scientific presentation, responding to questions, interacting with an audience, giving and receiving formative feedback. Instructor: Bjorn Sanstede. Senior Scholar: Jazlyn Nketia. Advanced registration required.

  • Jul
    18
    12:00pm

    DSCoV Workshop

    164 Angell Street

     

    Noam Peled, Research Fellow at the Martinos Center for Biomedical Engineering

     

    Neuroimaging Multi-Modal Analysis and Visualization

    MMVT (mmvt.org) is a free open-source 3D interactive tool for researchers who wish to have a better understanding of neuroimaging anatomical and functional data, both in time and space.

  • Jul
    19
    8:00am - 11:30am

    REDCap Workshop: “Creating Databases and Using Surveys”

    Rhode Island Hospital Grads Dorm 593 Eddy Street Providence RI

    Hone your REDCap skills in this upcoming workshop, “Creating Databases and Using Surveys,” presented by Advance-CTR.

    This workshop 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.

    Registration is required as space is limited. 

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Jul
    23
    4:00pm - 6:00pm

    Thesis Defense: Learning to ground natural language instructions to plans (Naked Gopalan)

    Watson Center for Information Technology (CIT)
    In order to intuitively and efficiently collaborate with humans, robots must learn to complete tasks specified using natural language. Natural language instructions can have many intentions, for example, they can specify a goal condition, or provide guidance to achieve the goal, or provide constraints that must be satisfied in achieving goals. Given a natural language command a robot needs to ground the instruction to an action sequence executed in the environment, which satisfies the request along with all its constraints and guidances. In this work I will address the problem of grounding natural language instructions to plans with different approaches, all of which depend upon predicates from first order logic. I will first describe a hierarchical planner for planning in large state spaces. I will then describe different procedures to ground natural language for hierarchical and constraint based tasks. We finish with looking at an approach that learns symbols and their natural language groundings using demonstrations and instructions. This allows the agent to follow novel instructions in unseen environments without hand-specified symbols.
    Host: Professor Stefanie Tellex
  • Aug
    12
    2:00pm - 3:30pm

    REDCap Workshop: “Advanced Users, Tips & Tricks”

    Rhode Island Hospital Grads Dorm 593 Eddy Street Providence RI

    Hone your REDCap skills in this upcoming workshop, “Advanced Users: Tips & Tricks,” presented by Advance-CTR.

    This workshop is geared toward intermediate to advanced users (those that have built databases before). Prior to class attendees will be given the chance to choose topics from a preset list. 

    Registration is required as space is limited.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Aug
    13
    9:00am - 10:30am

    REDCap Workshop: “Getting Started with Creating Databases”

    Rhode Island Hospital Grads Dorm 593 Eddy Street Providence RI

    Hone your REDCap skills in this upcoming workshop, “Getting Started with Creating Databases,” presented by Advance-CTR.

    The workshop offers an introduction to building databases in REDCap and covers basic concepts and best practices for building a database for your clinical research question. This workshop will be helpful to anyone building a new project in REDCap, and to those who have been tasked with managing or maintaining an existing database. Time at the end of class will be dedicated to individual questions. Requisites: Attendees must watch three introductory videos before class.

    Registration is required as space is limited. 

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Aug
    27
    All Day

    Beginning of Graduate School Orientation

    > No location for this event

    Beginning of Graduate School Orientation

    Fall Semester
  • Opening Convocation at 4:00 p.m. Registration of new students for the first semester (7:00 pm to midnight).

    Fall Semester
  • Classes of the first semester begin. Web registration begins at 8:00 a.m.

    Fall Semester
  • Scientific Computing Seminar

    Friday, September 6, 2019
    11:00am
    Room 108, 170 Hope Street

    Ming-Jun Lai , University of Georgia

    Title: Sparse Solutions and Their Applications

    Abstract : We shall first explain the study of the sparse solution to underdetermined linear system. Several convex minimization and nonconvex computational methods will be explained. These include$\ell_1$-minimization,Orthogonal Matching Pursuit(OMP), alternating projection method(AP) and their variations. Next we shall explain some applications, sparse solution for numerical solution of PDE, matrix completion, and phase retrieval problem.

  • Sep
    6
    1:00pm

    Hackathon on Opioid Overdose Prevention

    Rhode Island College

    The COBRE on Opioids and Overdose will host this inaugural hackathon event to help find solutions or “hacks” to address the opioid overdose crisis in Rhode Island and beyond. Students, professionals, and community members are all welcome to compete for cash prizes of $5,000 for the first place team, $3,000 for the second place team, and $2,000 for the third place team.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Sep
    8
    3:00pm - 3:30pm

    Getting Started with CareerLAB

    Salomon Center for Teaching

    This is a fun brief overview, of how CareerLAB works, what you should be focused on, and most importantly what “noise” you should be ignoring. One of the big road blocks in student’s accessing our fantastic services is understanding how we help students and how the career development process works while you’re in college. This clever little program will explain that.

  • Sep
    10
    2:00pm - 3:30pm

    REDCap Workshop: Getting Started with Creating Databases

    Rhode Island Hospital Grads Dorm 593 Eddy Street Providence RI

    Hone your REDCap skills in this upcoming workshop, “Getting Started with Creating Databases,” presented by Advance-CTR.

    The workshop offers an introduction to building databases in REDCap and covers basic concepts and best practices for building a database for your clinical research question. This workshop will be helpful to anyone building a new project in REDCap, and to those who have been tasked with managing or maintaining an existing database. Time at the end of class will be dedicated to individual questions. Requisites: Attendees must watch three introductory videos before class.

    Registration is required as space is limited.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Sep
    11
    11:00am - 12:00pm

    Child & Adolescent Psychiatry Grand Rounds

    Bradley Hospital

    “Is the Next School Shooter Sitting Before Me?”

    Critical Issues in Threat Assessment in Children & Adolescents

    Deborah M. Weisbrot, M.D., DFAACAP

    Clinical Professor of Psychiatry, Stony Brook Medicine

    Consulting Psychiatrist, Western Suffolk BOCES Schools

    Wednesday, September 11, 2019
    Bradley Hospital ◊ Pine Room ◊ 11:00 am - 12:00 pm

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

    Identify the essential aspects of threat assessment in children and adolescents; Identify critical warning behaviors and psychological patterns which need to be considered in threat assessment; and Discuss the complexities and challenges involved in the threat assessment process.

    Disclosure: Dr. Weisbrot has no financial relationship to disclose.

    This presentation will be teleconferenced from the Pine Room at Bradley Hospital to APC-969 and Coro West, Suite 204, Conference Room 2.186

    This activity is not supported by a commercial entity.

  • 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.

    Nicola Neretti, Ph.D., is an Associate Professor of Molecular Biology, Cell Biology, and Biochemistry at Brown University, and is one of the COBRE Center for Computational Biology of Human Disease’s success stories.  He graduated from the COBRE program by receiving peer-reviewed funding and he has been promoted to Associate Professor.  

    Dr. Neretti will present his talk entitled “Genomic and Epigenomic Instability in Cellular Senescence and Aging”.

    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 of 164 Angell Street. Any Brown ID should work during business hours.

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Research
  • Sep
    12
    12:00pm - 1:00pm

    Translational Research Seminar Series

    70 Ship Street

    Special Seminar!

    Community Engagement Studios: A Structured Approach to Obtaining Meaningful Feedback from Stakeholders to Inform Your Research 

    Presented by: Amy Nunn, ScD; Jacquelyn Fede, PhD; and Sharon Rounds, MD

    Community Engagement Studios provide researchers with the opportunity to receive direct feedback from community representatives about their particular health research topic. The studios help investigators align their work with community health priorities and engage with communities in culturally sensitive ways.

    In this seminar, the speakers will provide an introduction to Community Engagement Studios – what they are, how they work, and what an effective studio looks like, with examples. Attendees will have the opportunity to ask questions and get feedback from the speakers about their research ideas, and discuss any potential barriers that may exist to doing community-engaged research within their specific research area.

    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
  • Esteban Moro, MIT Media Lab and Universidad Carlos III (Madrid)

    Segregation is hurting our societies and specially our cities, where the fact that we live apart from other racial, economical or social groups carries tremendous economical and societal consequences. Not only for the people living in poor neighborhoods, but for the region as a whole. Most studies still describe people’s segregation patterns using census areas. However, encounters between people happen in places, not census areas, so our understanding of segregation still relies on very coarse-grained spatial description of how people interact or encounter in our cities. Using a massive dataset of high spatial resolution movements of 4.5 million people in 11 of the largest metropolitan areas in the US we have studied how encounters of different economic groups happen in our cities to determine the economic segregation at the level of places (venues) and individual users. We’ve found that some type of places (some restaurants, education, religious places) are constantly segregated across US, while some other (art exhibits, science museums, hospitals, etc.) are not. Furthermore, we were able to model individual segregation and found that most of the segregation/isolation that individuals experience in their daily lives does not depend on where they live, but on their individual behavioral patterns (type of places visited, social exploration, etc.) We discuss the implications of our results in the context of future development of areas and in the ever-changing evolution of our cities.

    Esteban Moro is a researcher, data scientist and professor at MIT Media Lab and Universidad Carlos III (UC3M) in Spain. He was previously researcher at University of Oxford. A native from Salamanca (Spain) he holds a PhD in Physics and is an affiliate faculty at Joint Institute UC3M-Santander on Big Data at UC3M and the Joint Institute of Mathematical Sciences (Spain). He has published extensively throughout his career (more than 60 articles) and led many projects funded by government agencies and/or private companies. Esteban has served as jury in many Data Science and Big Data challenges, is editor of several journals and has advised many PhD students.

    Esteban’s work lies in the intersection of big data and computational social science, with special attention to human dynamics, collective intelligence, social networks and urban mobility in problems like viral marketing, natural disaster management, or economical segregation in cities. Apart from his academic career he has worked closely with companies like Twitter, Telefónica, and BBVA in the use of massive datasets to understand problems like how humans communicate, how political opinion spreads in social networks or building alternative wellbeing indexes. He has received numerous awards for his research, including the “Shared University Award” from IBM in 2007 for his research in modeling viral marketing in social networks and the “Excellence in Research” Awards in 2013 and 2015 from UC3M.

    Esteban’s work has appeared in major journals including PNAS and Science Advances and is regularly covered by media outlets such as The Atlantic, The Washington Post, The Wall Street Journal, El País (Spain).

  • Sep
    16
    6:00pm

    Math+Art Panel Discussion

    121 South Main Street

    Please join us at ICERM to enjoy this second 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.

  • Sep
    19
    8:00am - 11:30am

    REDCap Workshop: Creating Databases & Using Surveys

    Rhode Island Hospital Grads Dorm 593 Eddy Street Providence RI

    This 3.5 hour 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 directly after you register.

    Registration is required as space is limited. 

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Sep
    19
    9:00am - 10:00am

    Special Workshop for Faculty Entrepreneurs

    Brown University Medical Education Building (Alpert Medical School)

    Don’t miss this special workshop for investigators who are interested in commercialization and entrepreneurship.

    Curtis Sprouse, president and CEO of the Institute for Biomedical Entrepreneurship (IBE), will discuss the program’s 5-day certificate program, which provides researchers in the life sciences with practical, hands-on knowledge and skills that help them evolve their innovations from lab to market.

    At the conclusion of the workshop, attendees will be invited to apply for an upcoming certificate program. Attendee sponsorship is contingent upon available funding.

    Investigators who have completed the certificate program will also be in attendance to answer questions and discuss their experience.

    Who Should Attend this Workshop:

    Early-career investigators, physician scientists, and other clinical and translational researchers at Brown, URI, and the affiliated hospitals in Rhode Island who are interested in entrepreneurship are encouraged to attend this workshop.

     Workshop is free but registration is required. 

    This event is sponsored by the Division of Biology and Medicine with funding provided through the NIH DRIVEN award. NIH Grant Number: 1UT2GM130176-02

    Biology, Medicine, Public Health, Careers, Recruiting, Internships, Training, Professional Development
  • Lisa LaVange, PhD
    Sep
    23
    3:00pm - 4:00pm

    Statistics C. V. Starr Lecture: Lisa LaVange, PhD

    121 South Main Street

    Lisa LaVange, PhD

    bio:  Lisa LaVange, PhD, is Professor and Associate Chair of the Department of Biostatistics in the Gillings School of Global Public Health at the University of North Carolina at Chapel Hill. She is also director of the department’s Collaborative Studies Coordinating Center (CSCC), overseeing faculty, staff, and students involved in large-scale clinical trials and epidemiological studies coordinated by the center. From 2011 to 2017, Dr. LaVange was director of the Office of Biostatistics in the United States Food and Drug Administration (FDA) Center for Drug Evaluation and Research (CDER). There, she oversaw more than 200 statisticians and other staff members involved in the development and application of statistical methodology for drug regulation. She was a leader in developing and assessing the effectiveness and appropriateness of innovative statistical methods intended to accelerate the process from drug discovery to clinical trials to FDA approval and patients’ benefit, with a particular focus on rare diseases. Prior to her government and academic experience, she spent 16 years in non-profit research and 10 years in the pharmaceutical industry. Dr. LaVange is an elected fellow of the American Statistical Association (ASA) and was the 2018 ASA President.

    Abstract:   Real-world Data, Statistical Innovation, and Evidence Generation

    As a result of the 21st Century Cures Act and the Prescription Drug User’s Fee VI Act, both passed into law in 2016, the FDA launched several important initiatives. The first involves real-world evidence generation from real-world data in both the pre- and post-market settings. This initiative reflects the desire to use existing health care data and data from other non-standard sources in lieu of or to supplement data collected as part of a research study to support product approval or expansion to new indications. Patient advocacy groups and medical researchers alike view the rich data streams that are becoming increasingly available as too big to ignore in terms of providing insight into a therapy’s safety and effectiveness. The second initiative reflects a desire to use innovative or otherwise non-traditional trial designs to generate substantial evidence in support of regulatory submissions. This initiative was intended to counter the belief that FDA reviewers were not often accepting of novel or complex trial designs, as shown by the lack of available guidance on the topic or examples that could be pointed to as successes. The third initiative reflects a desire to incorporate pharmacometric modeling into a drug development program to reduce the amount of data required to be collected in new clinical studies. The goal is to more effectively leverage what is already known about a drug, or able to be predicted with a high level of confidence, in planning the later stages of clinical development or expansion to new indications. In this seminar, I will review these initiatives, based on my involvement while at FDA in their development and roll-out, and discuss their impact on clinical research today. I will also discuss some recent initiatives at ASA that dove-tail nicely with the FDA initiatives and relate to opportunities for statisticians to take a leadership role in both finding and advocating for appropriate use of innovative solutions to problems in public health research.

    Biology, Medicine, Public Health, BioStatsSeminar, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Training, Professional Development
  • Poster for Haffenreffer Museum Student Group Interest Meeting.
    Sep
    23
    6:00pm

    Student Group Interest Meeting

    Manning Hall

    Do you like to think and talk about museums?

    Have you wondered what happens behind-the-scenes? 

    Are you intrigued by a career in museums? 

    Consider joining the Haffenreffer Museum Student Group! 

    Join us on Monday, September 23rd to talk to Haffenreffer Museum student group members, learn about their activities in the museum, and find out how you can get involved! 

    History, Cultural Studies, Languages, Humanities, Identity, Culture, Inclusion, Social Sciences, Student Clubs, Organizations & Activities
  • Sep
    27
    12:00pm - 1:00pm

    DSCoV Workshop: Keras Tutorial

    164 Angell Street

    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. Registration is necessary; limited to 40 participants.

     

    This week’s topic:

    Keras Tutorial taught by Drew Linsley, Postdoctoral Research Associate, Serre Lab, Brown University. 



    Friday, September 27, 12PM 164 Angell Street, 3rd Floor Seminar Space
    Co-organized by Thomas Serre and Center for Computation and Visualization.
    Sponsored by the Data
    Science Initiative.
    Pizza and soda will be served.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Mathematics, Technology, Engineering, Social Sciences
  • Sep
    28
    6:00pm - 8:00pm

    SOLD OUT: Illustrating Mathematics Open House at Waterfire

    121 South Main Street

    ICERM is pleased to be a part of Providence’s “Big Bang Science Fair” Waterfire event on September 28th. All are invited to attend our hands-on open house featuring many of our Illustrating Mathematics program participants. Come engage with these talented artists-in-residence and explore their work. Visualize mathematics through displays of art made using 3D printing, laser cutting, CNC routing, virtual reality, textiles, carving, painting, video, and more! Mingle in ICERM’s beautiful 11th-floor space along the river and enjoy a sweeping view of Waterfire. Tickets are required for entry. This event is SOLD OUT.

    The Math+Art open house themes and table leaders are:

    Computational Textiles
    Table leader: Sabetta Matsumoto

    Sabetta Matsumoto is an Assistant Professor in the School of Physics at the Georgia Institute of Technology. She uses differential geometry, knot theory and geometric topology to understand the geometry of materials and their mechanical properties. She is passionate about using textiles, 3D printing and virtual reality to teach geometry and topology to the public.
    https://matsumoto.gatech.edu/

    Help Build “FireStar” Installation
    Table leader: Glen Whitney

    Glen Whitney is passionate about every part of public mathematics — engagement, appreciation, and informal learning. After graduating from Harvard in 1989, Whitney did his graduate work in mathematical logic at UCLA and received a Ph.D. in 1994. He was on the faculty of the University of Michigan and subsequently became an applied mathematician at the quantitative hedge fund, Renaissance Technologies. In 2008, he left Renaissance and founded the National Museum of Mathematics (MoMath) in New York City. The museum opened its doors to the public in December 2012 and is nearing its millionth visitor. Whitney continues to work on a variety of projects, promoting public mathematics in fresh and innovative ways.
    https://www.mathforamerica.org/about/board/glen-whitney/

    3-dimensional Art
    Table leader: Saul Schleimer

    Saul Schleimer is a Reader in Mathematics at the University of Warwick (UK). His research interests include topology — the study of shapes, hyperbolic space — the so-called “non-euclidean geometry”, and group theory — the study of symmetry. He is especially interested in using computers to visualize objects and ideas in these areas.
    https://warwick.ac.uk/fac/sci/maths/people/staff/saul_schleimer/

    2-dimensional Art
    Table leader: Frank Farris

    Frank Farris is a professor in the Department of Mathematics and Computer Science at Santa Clara University. He is an award-winning author, artist, and speaker. He uses ideas from advanced mathematics, such as complex analysis and abstract algebra, to transform images of landscapes, flowers, or even his dinner into beautiful repeating designs.
    http://math.scu.edu/~ffarris/

    Math-in-Motion
    Table leader: John Edmark

    John Edmark, a lecturer in Stanford University’s Design Program, employs precise mathematics in the design and fabrication of his work. He does this neither out of a desire to exhibit precision per se, nor to exalt the latest technology, but because the questions he is trying to formulate and answer about spatial relationships can only be addressed with geometrically exacting constructions. Mathematical precision is an essential ally in his goal of achieving clarity.
    http://www.johnedmark.com/

    American Mathematical Society (AMS) Activity Table (hands-on for all ages)
    Table leaders: Representatives from the AMS

    AMS staff will lead Patterns: Parabolas & Polygons, a line drawing activity. Make and bring home beautiful geometric patterns from simple lines. The AMS, headquartered in Providence, RI, supports the mathematical sciences by providing access to research, professional networking, conferences and events, advocacy, and a connection to a community passionate about mathematics and its relationship to other disciplines and everyday life. Explore a wide range of offerings online.
    https://www.ams.org/

    This open-house is made possible with grants from the National Science Foundation, and the Alfred P. Sloan Foundation award G-2019-11406.

    Mathematics, Art, STEM, Research
  • Prasanta Pal,PhD
    Sep
    30
    3:00pm - 4:00pm

    Statistics Seminar, Prasanta Pal, PhD

    121 South Main Street

    Prasanta Pal, PhD, Research Project Director, U. Mass. Medical School, Adjunct Assistant Professor of Behavioral and Social Sciences, School of Public Health, Brown University

    Bio: I trained as a Physicist at Indian Institute of Technology, Kharagpur, India and Yale University School of Engineering, New Haven, CT. Starting with computer simulation on super-conducting quantum computer at Yale , my PhD work was focused on large scale computer simulations of soft matter particle systems. I briefly worked as a postdoc at Yale MRI center to study blood flow dynamics in human heart. Then I head started the technological platform of the startup company “Mind sciences” to build mobile applications like “Craving to Quit” for addiction treatment through mindfulness interventions. In parallel I built high-density real-time EEG neuro-feedback systems to provide mental-training to different population groups. This has been part of large scale clinical trials and demonstrated in commercial program like CBS 60 minutes. Currently I’m developing tools for big-data, 4D visualization of human mind and its quantification using AI and computational geometry.

    Abstract: “Chasing the Mind”

    Background: the quest to understand the mind is age-old and unresolved. Although the “Mind” is central to nearly all human activity, the quest for a complete scientific understanding of “how the human mind works” remains unresolved. In science, much progress has been made to understand the nature and function of the mind using various techniques. Useful models of mental and emotional function have emerged, with an increasingly detailed picture of the brain-body connection and its many roles in health and survival.

    Opportunity: Mind-body research reveals inner order, while technology opens frontiers for analysis.  More recently, mystics of the east and clinicians of the west have come to recognize the common platform through which they seek to promote mental well being. Increasingly, the exploration of one’s inner self (e.g., through inner query and mental-training) can complement external tools like thermometers and drug regimens. The old notion of body-based medicine has evolved into mind-body medicine. Clinically, successful programs like MBSR (Mindful Based Stress Reduction) developed by Jon Kabat Zinn are geared towards harmonizing the physical and mental dimensions of human existence. Importantly, the inner order and wholeness perceived by mystics is gaining solid scientific footing and paving the way for a revolutionary new age of medicine.

    On the other hand, the rapidly evolving knowledge-base of machine-learning, data science and technology are collapsing the boundaries between traditional academic disciplines. As a result, commercially available devices like MUSE for meditation and EEG sleep monitors are device incarnations to play with “Mind dynamics”.

    Challenge: Reductionist frameworks miss hidden order in the mind’s complexity.  The human brain is by far the most sophisticated computing-device known to science. High quality data measured from the human brain via EEG like sensors can effectively be utilized to create the cartography of the human mind.

    Although simplistic reductionist models have very successfully permeated the domains of physical sciences, it is often limited in scope to explain the human mind. As an alternative, we introduce a more holistic approach to pave the cartographic building blocks of the human mind using high dimensional data collected through EEG sensors attached to the human brain.

    Our work: New imaging and computational techniques to visualize the mind’s wholeness.  As data scientists and technologists, our work is to build tools to visualize and investigate the human mind with as much scientific and mathematical rigor as complex fields like rocket science. The holy grail is to map out the deeper-most thought patterns in the human mind on our favorite mobile device. What has been inner can be unfolded to the outer.

    In this work, we describe our unique journey to understand the human mind starting with a simple noisy one-dimensional time series plot of a small EEG data segment and, from there, building models with infinite-dimensional function space to characterize and visualize the experientially active human mind. We demonstrate that the inner journey of the self is also reciprocally, a number crunching exercise. With these tools, when fully developed, we can transform soulless-selfies to the speckles of our blissful inner-selves.

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

  • 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
  • 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.

  • 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
    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
  • 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
    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.

  • 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 Venkataramana n, 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
    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
  • 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

  • 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
  • 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
  • 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
    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
  • 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
    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

  • 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!
  • 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.

  • 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

  • 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.

  • 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.

  • 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
  • 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
  • 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
  • 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
  • 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.

  • 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
    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
  • 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
  • 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
  • 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
    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
  • 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.

  • 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
  • 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
    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
  • 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
    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.

  • 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
    11
    12:00pm - 1:00pm

    Workshop - Version Control with Git

    180 George Street

    A practical introduction to version control for software management using Git. Topics covered include: creating a repository, checking the status of a repository, committing changes, viewing changes, reverting to older versions of files, and setting up a remote repository. Register online .

    Computing, HPC, Research
  • 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.

  • 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

  • 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
  • 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.

  • 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.

  • 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
  • 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
  • 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.

  • 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
  • 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
    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
    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
    25
    12:00pm - 1:00pm

    Workshop - Building Software on Oscar

    180 George Street

    Learn how to build and install software packages on Oscar. Topics covered include: make, cmake, compiler options on Oscar, pip, building python packages from source, and python virtual environments. Register online .

    Computing, HPC, Research
  • 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.

  • 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
  •  

    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.

  • 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

  • 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
  • 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
  • Dec
    9
    12:00pm - 1:00pm

    Workshop - Using Matlab on Oscar

    180 George Street

    An introduction to using Matlab on Oscar. Topics covered include: working with Matlab interactively on Oscar, using the Matlab GUI, using Matlab in batch jobs, and working with Parallel Toolbox. Register online .

    Computing, HPC, Research
  • 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
    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.

  • 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
    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
  • 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
  • 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..

  • Title and abstract TBA.

  • Jan
    29
    4:00pm

    Data Wednesday: Achal Neupane

    164 Angell Street

    Title and abstract TBA.

  • 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
  • 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
  • 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!

  • 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
    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

  • 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
  • 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.

  • 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.

  • 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.

  • 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
    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.

  • 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
    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. 

  • 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
    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!

  • 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

    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
    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.

  • 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.

  • 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.

  • 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
  • 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.

  • 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
  • Title and abstract TBA.

  • 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 

  • 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
  • Title and abstract TBA.

  •  

    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.

  • 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
  • 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.

  • Title and abstract TBA.

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

  • Title and abstract TBA.

  • Title and abstract TBA.

  • 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
  • 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

  • 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.

  • 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

  • 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.
  • 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.

  • 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.

  • 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
  • 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
  • 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
  • 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.

  • 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.

  • 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