Past Events

  • Peter Mueller, PhD
    Oct
    3
    12:00pm - 1:00pm

    Statistics Seminar | Peter Mueller, Ph.D.

    School of Public Health, 121 South Main Street

    Talk Title: Bayesian Nonparametric Common Atoms Regression for Generating Synthetic Controls in Clinical Trials

    Abstract: We develop a Bayesian nonparametric approach for creating synthetic controls from real world data (RWD) to supplement treatment-only single arm trials. We introduce a Bayesian common atoms regression model that clusters covariates with similar values across different treatment arms. Exploiting the common atoms structure, we propose a density free importance sampling scheme to sample a subpopulation of the RWD such that the covariates in the subpopulation have the same distribution as the actual patients, allowing for a valid treatment comparison. Inference under the proposed common atoms mixture model can be characterized as a stochastic stratification by propensity score (for selection into control or treatment arm). The proposed design is implemented for glioblastoma trials.

    *Light refreshments will be served

    Biology, Medicine, Public Health, Research
  • Oct
    3

     

    Luca Belli

    Staff Machine Learning Researcher at Twitter

    Abstract: Content on Twitter’s home timeline is selected and ordered by personalization algorithms. By consistently ranking certain content higher, these algorithms may amplify some messages while reducing the visibility of others. There’s been intense public and scholarly debate about the possibility that some political groups benefit more from algorithmic amplification than others. We provide quantitative evidence from a long-running, massive-scale randomized experiment on the Twitter platform that committed a randomized control group including nearly 2 million daily active accounts to a reverse-chronological content feed free of algorithmic personalization. We present two sets of findings. First, we studied tweets by elected legislators from major political parties in seven countries. Our results reveal a remarkably consistent trend: In six out of seven countries studied, the mainstream political right enjoys higher algorithmic amplification than the mainstream political left. Consistent with this overall trend, our second set of findings studying the US media landscape revealed that algorithmic amplification favors right-leaning news sources. We further looked at whether algorithms amplify far-left and far-right political groups more than moderate ones; contrary to prevailing public belief, we did not find evidence to support this hypothesis. We hope our findings will contribute to an evidence-based debate on the role personalization algorithms play in shaping political content consumption. Read the paper. 

    Bio: Luca is the co-founder and Research Lead for the Machine learning ethics transparency and accountability team within Twitter. His research group is focused on studying the impact of Twitter’s machine learning systems, with special attention on feedback loops and algorithmic amplification. He’s very opinionated about Italian food.

    Lunch will be provided, or bring your own!

    Government, Public & International Affairs, Research, Social Sciences
  • Sep
    30
    3:00pm

    Data & Donuts

    164 Angell Street, Providence, RI 02912

    Data & Donuts

    Join the DSI every Friday at 3pm for donuts and a “Data Open Mic”. The format of this series will allow colleagues to connect informally and will feature short talks on research or campus resources in data science. 

  • The new NIH Policy on Data Management and Sharing goes into effect on January 25, 2023. How should researchers prepare for changes in proposal development, data collection, and depositing data? How will the policy impact research, including new pre- and post-award engagement with NIH repositories, and updated timelines for data preparation and depositing?

     

    On September 30th from 1:30pm-2:30pm join Brown University’s Arielle Nitenson, Assistant Director of Research Integrity, & Andrew Creamer, Science Data Specialist, for an educational seminar regarding this new policy.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Psychology & Cognitive Sciences, Research, Social Sciences, Training, Professional Development
  • Sep
    28
    6:00pm

    Data Science Initiative DUG Fall Welcome Event

    164 Angell Street, 3rd Floor

    All students welcome!

    If you are an undergraduate student from ANY concentration, interested in data science or the Data Fluency Certificate, we invite you to come to the first meeting of the Data Science DUG. 

    The Data Science Initiative Departmental Undergraduate Group (DUG) serves students across all concentrations (including the Humanities, Arts, Social and Behavioral Sciences, etc.) who are interested in incorporating data science into their Brown experience.

    We’ll plan for future events and hear from guest speaker Suresh Venkatasubramanian
    Professor, Data Science Initiative and Computer Science

    Pizza and refreshments will be served. Please sign up ahead of time so we can be sure to have enough! 

    Student Clubs, Organizations & Activities
  • IFL Lecture Series
    Sep
    28
    5:30pm - 7:00pm

    IFL: Cambridge Analytica Four Years Later

    Brown University Pembroke Hall

    The School of Public Health’s Information Futures Lab invites you to attend the second Information Futures Lab panel discussion and Q&A!

    Wednesday, September 28, 2022 5:30 - 7:00 pm ET
    Pembroke Hall | Rm 305
    Lecture followed by reception 
    Livestream Link Here 

    In 2018, the world learned that personal data belonging to millions of Facebook users was collected by consulting firm Cambridge Analytica. Four years later, what is the state of mass data collection, targeted advertising, and algorithmic transparency, especially as it relates to public health?

    Please register to let us know if you’ll be attending in person or online.

    Biology, Medicine, Public Health, Government, Public & International Affairs, International, Global Engagement, Mathematics, Technology, Engineering, Research
  • COBRE Center 2022-2023 Pilot Award Call for Applications

    Complete guidelines and application details are available on the COBRE website under EVENTS:

    https://www.brown.edu/research/projects/computational-biology-of-human-disease/events

    About:

    The goal of the COBRE Center for Computational Biology of Human Disease (CBHD COBRE) Institutional Pilot Award Program is to identify and support activities of talented junior investigators working on human disease-related questions that require computational analyses of complex data sets. Awardees will gain access to the Computational Biology Core (CBC) of staff data scientists who will assist with data analyses. In addition, awardees may be considered for recruitment to a Project Leader position in the CBHD COBRE program.

    Amount and Duration of Award

    We expect to fund at least two 1-year projects for $50,000 each. Indirect costs and faculty salaries are not allowed, but applicants must indicate some level of effort, usually 1 to 2%.

    Deadlines

    Applications must be completed using UFunds due by 8:00 a.m. on Monday, October 31, 2022

    Internal Advisory Committee Review Due Date: November 14, 2022

    External Advisory Committee Review Due Date: December 1, 2022

    Estimated Application Award Date: January 1, 2023

    Eligibility

    Applicants for this Pilot Program must hold a faculty appointment (or equivalent) at Brown University or its affiliated hospitals, and propose work that is consistent with the goals of the CBHD COBRE.

  • Register for the LHS Scholar Webinar on Wednesday, September 26, 2022, at 1:00 PM ET, featuring Q&A with the program leaders Rosa Baier & Janet Freburger. Please review the details of this year’s RFA and available partners once it is posted in mid-September.

    About the LHS Scholar Program:

    The Learning Health Systems (LHS) Scholar Program partners rehabilitation researchers with health system stakeholders for a 12-month period to prepare for research or a quality improvement project on a priority topic identified by the health system. Scholar activities during the year include: a) developing a relationship with health system stakeholders; b) developing an understanding of the context of the health system; c) identifying available data and addressing access issues; d) developing Learning Health System research competencies; and e) formulating a plan/proposal with the health system to address a system-identifiedpriority. Each LHS Scholar is paired with a faculty mentor who offers guidance to the Scholar and health system as they begin to develop a plan to engage in LHS research/quality improvement. When the year-long scholar period ends, the Scholar and health system will be well-positioned to advance the work, for example, by seeking pilot funding through LeaRRn or other mechanisms.

  • Efforts to translate evidence-based digital health interventions from research to practice have struggled with sustained consumer engagement and the successful implementation of these tools within their targeted systems of care. Human-centered design involves collaborating deeply with end-users throughout the process of design and testing to ensure the intervention and its implements meets end-users’ needs and preferences, which can in turn increase uptake and engagement. This presentation will describe the human-centered design process and several design methods to inform ways to increase engagement in intervention design and delivery.

    Speaker Bio

    Andrea K. Graham, PhD (she/her) is Assistant Professor in the Center for Behavioral Intervention Technologies at Northwestern University’s Feinberg School of Medicine, with an affiliation in the Center for Human-Computer Interaction + Design. Her program of research focuses on the design, optimization, and implementation of evidence-based digital mental and behavioral health interventions. She applies human-centered design methods to design digital tools that meet stakeholders’ needs and implementation plans that support the integration of digital interventions into practice. She also is interested in understanding issues such as the cost of treatment that impact adoption of interventions in practice, and in training individuals to deliver evidence-based interventions.

     

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

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Psychology & Cognitive Sciences, Research
  • Sep
    23
    12:00pm - 1:00pm

    Health Informatics Seminar Series: What is Biomedical Informatics?

    Brown University Medical Education Building (Alpert Medical School)

    Brown Center for Biomedical Informatics

    Health Informatics Seminar Series: What is Biomedical Informatics?

    Elizabeth Chen, PhD, Carsten Eickhoff, PhD

    Hamish Fraser, MBCHB, MSc, & Neil Sarkar, PhD, MLIS

     
    Biology, Medicine, Public Health, Research
  • IFL
    Sep
    22
    5:30pm - 7:00pm

    IFL: Can regulation solve the problem of misinformation?

    Stephen Robert ’62 Hall, 280 Brook Street

    The School of Public Health’s Information Futures Lab and the National Conference on Citizenship’s Algorithmic Transparency Institute invite you to attend the first of their upcoming IFL Lecture Series.

    Thursday, September 22, 2022
    5:30 - 7:00 pm ET
    Stephen Robert ’62 Hall, 280 Brook Street, True North Classroom (101)
    Lecture followed by refreshments and networking
    Livestream Link: https://www.youtube.com/watch?v=CkxtsAYIZtM 

    Moderated by Professor Claire Wardle, this event will explore the current regulatory landscape in the context of online information. Over the past year, the EU Commission’s Digital Services Act and the Online Safety Bill in the UK have provided some possible regulatory roadmaps. A distinguished panel will consider the impact of these policy decisions in terms of affecting online misinformation, as well as the wealth of draft bills percolating in the US, both at local and national levels.

    Please register to let us know if you’ll be attending in person or online.

    Next Talk in the series:

    Wednesday, September 28, 2022 5:30 - 7:00 pm ET
    “Cambridge Analytica Four Years Later”
    Location TBA | Livestream will be available

    Biology, Medicine, Public Health, Government, Public & International Affairs, International, Global Engagement, Mathematics, Technology, Engineering, Research
  • Headshot Portrait of Katie Stack Morgan Requesters: Katie Stack Morgan Photographer: R. Lannom Da...Credit: NASA/JPL-CaltechExploring and Sampling Potential Biosignatures with the Mars 2020 Perseverance Rover

    DEEPS is proud to welcome Dr. Kathryn Stack Morgan from the NASA Jet Propulsion Laboratory as our special guest speaker for the 2022 Thomas A. Mutch Lecture.

    This talk is open to everyone, and will be followed by a reception in Lincoln Field, Room 120.

    The Thomas “Tim” Mutch Memorial Fund was established in 1981 by his family and friends to honor Tim’s memory as a scholar, teacher, explorer, author, administrator, and involved citizen. One of the purposes of the fund is to honor those who have shown intellectual courage and resolve in exploring important areas of the Solar System, and to bring them to Brown to share their results through the Thomas A. Mutch Lectures.

    Physical & Earth Sciences
  • Sep
    15
    2:30pm - 4:30pm

    Dissertation Defense of Biostatistics Doctoral Candidate Xiaoyu Wei

    School of Public Health, 121 South Main Street

    Biostatistics Doctoral Candidate Xiaoyu Wei

    Incorporating Biological Knowledge into the Statistical Analysis for Genomic Studies

    Please join us as Biostatistics doctoral candidate Xiaoyu Wei defends his thesis, “Incorporating Biological Knowledge into the Statistical Analysis for Genomic Studies.”

    The development of high-throughput sequencing technology enables a deeper understanding of gene regulatory mechanisms by performing statistical analyses of genomic data. Most traditional statistical approaches treat all genes identically and independently, and overlook the complicated relationship among genes, which are regulated through biological pathways. Functional genomic studies have elucidated such relationships, and the information is now stored in many public databases. Utilizing these known biological knowledge could potentially improve the statistical analysis and the power for biological discoveries. In this dissertation, we address incorporating biological knowledge into the statistical analysis of high-throughput sequencing data from three different aspects. First, we focus on the differential expression analysis in complex study designs and repeated measures. We provide a new perspective on detecting differential expression in these situations with visualizations. We also propose a weighting approach to address heteroscedasticity issues in genomic studies to improve power. Identifying differentially expressed genes may not be the ultimate goal, and researchers are often interested in learning about phenotypic outcomes. Therefore, in the second chapter, we investigate the mediation mechanisms of genes between the treatment and the outcome. A network-constrained regularization is applied to the variable se- lection in the mediator models. Finally, to further understand the relative strength of association within the networks, we employed a deep learning model, variational autoencoders, to learn the latent networks in scRNA-Seq data. Constraints are imposed on the neural network structure to reflect the biological knowledge. The original high-dimensional input data can be compressed into a lower-dimensional representation with biological interpretations. The performances of proposed methods are evaluated through simulation studies, and applications to high-throughput sequencing data are provided to demonstrate the use of proposed methods.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Graduate School, Postgraduate Education
  •  

    DATA MATTERS SEMINAR SERIES

     

    Featuring
    Claire Wardle

    Professor of the Practice of Health Services, Policy, and Practice, Brown University

     

    Discussion led by
    Suresh Venkatasubramanian

    Professor of Data Science and Computer Science, Brown University

     

    Some Honest Truths:

    Reflecting on the ‘Fight’ Against Misinformation

  • Sep
    12
    12:00pm - 2:00pm

    MRI Users Meeting

    164 Angell Street, Providence, RI 02912

    We are pleased to announce that we will be resuming in-person MRI Users Meetings (with a hybrid Zoom option), with our first meeting on September 12, at 12 p.m.

    We will present an update on the current activities and resources at the MRF, with a focus on XNAT, data transfer, and management systems. There will be an opportunity for community feedback and questions about any MRF-related issues you may have.

    Lunch will be provided!

    Please use the link below to register before 5:00 p.m. on Thursday, September 8. We look forward to seeing you.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research
  • Ani Eloyan
    Sep
    12
    12:00pm - 1:00pm

    Chair’s Invited Seminar in Statistics | Ani Eloyan, Ph.D.

    School of Public Health, 121 South Main Street

    The Chair’s Invited Seminar in Statistics is designed to showcase outstanding research being conducted by faculty in the Department of Biostatistics at Brown, and to provide an opportunity for the larger Brown community to learn about the work being conducted in our department. It will be delivered each year by a current faculty member or affiliate of the Department of Biostatistics.

    Talk Title: Imaging and Clinical Biomarker Estimation in Alzheimer’s Disease

    Abstract: Estimation of biomarkers related to disease classification and modeling of its progression is essential for treatment development for Alzheimer’s Disease (AD). The task is more daunting for characterizing relatively rare AD subtypes such as the early-onset (AD) and others. In this talk, I will describe the Longitudinal Alzheimer’s Disease Study (LEADS) intending to collect and publicly distribute clinical, imaging, genetic, and other types of data from people with EOAD, as well as cognitively normal (CN) controls and people with early-onset non-amyloid positive (EOnonAD) dementias. I will discuss factor-analytic methods for estimation of clinical biomarkers of AD and their use for modeling differences in longitudinal trajectories of clinical deterioration between CN, EOAD, and EOnonAD groups in LEADS. Finally, I will discuss our work in leveraging magnetic resonance imaging and positron emission tomography data to characterize distributions of white matter hyperintensities in people with EOAD and to obtain imaging-based biomarkers of disease trajectories of AD subtypes.

    *Light refreshments will be served

    Biology, Medicine, Public Health, Research
  • With Eleftherios Mylonakis, M.D., Ph.D. - Charles C.J. Carpenter Professor of Infectious Disease Chief, Infectious Disease Division, Assistant Dean for Outpatient Investigators.

    Advising, Mentorship, Biology, Medicine, Public Health, Education, Teaching, Instruction, Research
  • Call for Applications! Apply for the Advance-CTR Advance-R program

    About the Program

    We are excited to offer faculty submitting applications for their first R-award (and similar) the opportunity to have their proposals reviewed by an internal study section consisting of content experts and experienced NIH reviewers prior to external submission. Written NIH-style feedback, as well as additional comments on grantsmanship, from the internal study section will be provided to help researchers improve the quality of their application. Additionally, we will provide training on issues related to the submission process.

     

    Key Dates & Deadlines

    • Request to Participate Form Available: August 9th

    • Request to Participate Due: September 6th

    • Notification of Selection for Program: on or before September 23rd

    Didactic Sessions:

    • September 28th from 1-2pm

    • October 12th from 1-2pm

    • October 26th from 1-2pm

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Dmitrijs Celinskis, Ph.D. Defense
    Jul
    19
    10:00am - 11:00am

    Thesis Defense: Dmitrijs Celinskis

    Building for Environmental Research and Teaching (BERT)

    Dmitrijs Celinskis

    Multisite and Multimodal Imaging Methods for Studying Spinal, Brain and Vascular Dynamics

    Advisors: David Borton, Ph.D. & Christopher Moore, Ph.D.

    Biology, Medicine, Public Health, Mathematics, Technology, Engineering, Research
  • Join Advance-CTR for two training modules on the ins and outs of NVivo and how to utilize the software for your study. The Advance-CTR Virtual NVivo Modules will be conducted on a Macbook and provide an introduction to the NVIV0 2020 software version.

    (Module 1 Scheduled for Wednesday, June 22)

    Module 2: NVivo Project Set-up
    Step-by-step demonstration on how to set up a new project, enter data, create codes, and conduct preliminary analysis.
    Wednesday, June 29, 2022
    10:00 a.m. - 11:00 a.m. (Open Q&A from 11:00 to 11:30 a.m.)
    Biology, Medicine, Public Health, Research, Teaching & Learning
  • Join Advance-CTR for two training modules on the ins and outs of NVivo and how to utilize the software for your study. The Advance-CTR Virtual NVivo Modules will be conducted on a Macbook and provide an introduction to the NVIV0 2020 software version.

    Module 1: Introduction and Overview to the NVivo Software
    A general overview and introduction on the NVivo software and its potential uses.
    Wednesday, June 22, 2022
    10:00 a.m. - 11:00 a.m. (Open Q&A from 11:00 to 11:30 a.m.)
    Biology, Medicine, Public Health, Research, Teaching & Learning
  • How to register. Please click on the ‘Apply with Cube’ link below under ‘Application Information’. Once you create a new account, please complete your profile and register for the event.

    On the registration page, you can indicate whether you are applying for funding or need assistance with travel and lodging. It is important to note that you are asked to upload a CV. You can, however, upload a document describing your name and institution. If you are a graduate student, your advisor can upload your letter of recommendation to https://app.icerm.brown.edu/Cube/advisor

    Conference description.

    Research in HIV continues to generate highly complex data structures. Examples include genomic sequences (both host and virus); individual medical records, which include such complications as irregular measurement, missing data, and unstructured text fields; medical images; social network data; and aggregated ‘super cohorts’ such as those coordinated by the IeDEA and CNICS consortia. Even the design and analysis of randomized trials require innovative techniques to enable optimal use of data that can be expensive and labor-intensive to collect.

    This symposium is designed to bring together statistical and data science researchers either working directly in the area of HIV or whose work has direct relevance to problems and data structures encountered in HIV research. We are particularly interested in engaging data science researchers in fields such as computer science, engineering, and applied mathematics, whose work in related areas might lead to innovative new approaches. Participants will gather for focused activities related to dissemination of new methods, formation of new collaborations, extended discussion to identify new challenges, and engagement of junior investigators.

    Finally, owing to investments by NIH and other funding agencies, the number of HIV-focused statisticians and data scientists from low- and middle-income countries is growing. The symposium also is designed to promote continued engagement between statistical scientists from the ‘global north’ and ‘global south’.

    Is there funding available? Yes. We have funding available for graduate students, postdocs, and those coming from low and middle income countries.

    Is there a virtual option? The conference is in-person but we are working to set up a virtual broadcast of the talks. In person applications will be given priority.

    Who can I contact with questions about Cube? Please email [email protected]

    Co-sponsored by Providence-Boston Center for AIDS Research and ICERM https://cfar.med.brown.edu/

  • The Summer 2022 Biomedical Informatics Journal Club

    Fridays at Noon

     

    This summer, the Biomedical Informatics Journal Club will feature selected readings from IMIA Yearbook of Medical Informatics: Managing Pandemics with Health Informatics: Successes and Challenges (2021)

    Each week the Journal Club will focus on 2 pre-selected articles with a 5-10 minute overview and a 20-25 minute discussion lead by a current student from the Biomedical Informatics Scholarly Concentration or BCBI faculty member.

    All attendees are encouraged to engage in open discussion about the selected articles including how the topics covered relate to their own project(s), research and/or clinical experience.

    • Week 1 (June 17, 2022): Editorial and History
    • Week 2 (June 24, 2022): Clinical Informatics Systems and Clinical Decision Support
    • Week 3 (July 8, 2022): Natural Language Processing and Research & Education
    • Week 4 (July 15, 2022): Knowledge Representation & Management and Human Factors & Organizational Issues
    • Week 5 (July 22, 2022): Managing Pandemics with Health Informatics and World Health Organization (WHO) Report

     

    *Journal Club will not be held on Friday July 1, 2022
    Biology, Medicine, Public Health
  • Jun
    8
    3:00pm - 5:00pm

    CDH Pitch Competition

    Zoom

    Join the Brown-Lifespan Center for Digital Health (CDH) and the Nelson Center for Entrepreneurship (NCE) for an afternoon of innovation and entrepreneurship. We have identified 8 teams of innovators within the Brown-Lifespan ecosystem to pitch their digital health applications and platforms to a panel of esteemed judges. The winning team(s) will walk away with up to $25,000 seed funding and ongoing mentorship from CDH leadership.

    These teams of junior investigators at Brown and Lifespan will be presenting their 5-min pitch for a digital health innovation that focuses on improving population health, public health, and/or health care delivery with an emphasis on health equity.

    Audience members will have the opportunity to submit feedback and vote on their favorite innovation!

    Biology, Medicine, Public Health, Entrepreneurship, Graduate School, Postgraduate Education, Psychology & Cognitive Sciences, Research
  • Dr. Guixing Wei
    May
    13
    12:00pm - 1:00pm

    Data Visualization Seminar

    Zoom

    Join Advance-CTR, S4 and the Brown Library for part two in this series exploring data visualization, its methodology, and application in biomedicine and health. The purpose of this series is to serve as a data visualization introduction for clinicians and others who may be interested in using these tools and methods in their research.

    “What GIS & Spatial Analysis can do for Health Research”
    Guixing Wei, Senior GIS Developer and Spatial Scientist
    The Spatial Structures in the Social Sciences (S4) Population Studies & Training Center

    Dr. Wei will provide an introduction to GIS, mapping and spatial epidemiology and their applications in health research. A brief survey of common GIS techniques in health applications will be presented to showcase how health studies can benefit from spatial perspectives and geo-analytics. During the talk, Wei will also introduce S4 and Brown’s GIS infrastructure and their supportive resources to the Brown community.

    Biology, Medicine, Public Health, Teaching & Learning
  • Adam Brickman, Ph.D.

    Professor of Neuropsychology
    Taub Institute for Research on Alzheimer’s Disease and the Aging Brain
    Department of Neurology
    Vagelos College of Physicians and Surgeons
    Columbia University
     


    Please note that this is a hybrid seminar in Smith-Buonanno room 106 and through Zoom (please email [email protected] for the link information).

     

    Bio:

    Adam M. Brickman, PhD is a tenured Professor in the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain and in the Department of Neurology at Columbia University. Dr. Brickman’s work primarily focuses on understanding the vascular contributions to cognitive aging and Alzheimer’s disease by integrating neuroimaging techniques with observational neuropsychological studies, basic neuroscience, and epidemiological approaches. He is also interested understanding sources of racial and ethnic disparity in Alzheimer’ disease, developing interventions for cognitive decline in aging, and designing neuropsychological instruments to assess cognition in older adults.

    Dr. Brickman leads neuroimaging efforts in several large community- and clinic-based observational studies, such as the Washington Heights Inwood Columbia Aging Project (WHICAP), the WHICAP Offspring Study, the Alzheimer’s Biomarker Consortium-Down Syndrome (ABC-DS), and others. Dr. Brickman is the Core Leader of the Columbia Alzheimer’s Disease Research Center Biomarker Core, which integrates fluid and neuroimaging based biomarkers into studies of Alzheimer’s disease and related disorder.

    Dr. Brickman completed his undergraduate studies in neuroscience and psychology at Oberlin College, his PhD in psychology/neuropsychology at the City University of New York, his clinical internship at Brown Medical School, and his postdoctoral training at Columbia University, where he has been on faculty since 2007.

    Biology, Medicine, Public Health, Research
  • May
    10
    11:00am - 12:00pm

    NIH Policy for Data Management and Sharing

    Virtual

    The new NIH Policy on Data Management and Sharing goes into effect on January 25, 2023. How are the departments at Brown that support researchers preparing for this new policy? How should researchers prepare for changes in proposal development, data collection, and depositing data? How will the policy impact research, including new pre- and post-award engagement with NIH repositories, and updated timelines for data preparation and depositing? In this session, we will give an overview of the new policy and Brown resources such as templates to help researchers with writing plans, tools for managing their data throughout a project, and sharing data during and after a project closes. Discussion led by Arielle Nitenson and Andrew Creamer.

    Biology, Medicine, Public Health, Research
  • May
    9
    12:00pm - 1:00pm

    Statistics Seminar | Yoav Benjamini, Ph.D.

    121 South Main Street

    Dr. Yoav Benjamini, Professor Emeritus of Applied Statistics at the Department of Statistics and Operations Research at Tel Aviv University, and a member of the Sagol School of Neuroscience and the Edmond Safra Bioinformatics Center.

    Replicability Issues in Medical Research: Science and Politics

    Selective inference and irrelevant variability are two statistical issues hindering replicability across science. I will review the first in the context of secondary endpoint analysis in clinical and epidemiological research. This leads us to discuss the debate about p-values and statistical significance and the politics involved. I will present practical approaches that seem to accommodate the concerns of NEJM editors, as reflected in their guidelines.
    I shall discuss more briefly the issue of addressing the relevant variability, in the context of in preclinical animal experiments, and the implication of this work about assessing replicability in meta-analysis.

    Major parts of this work done jointly with Iman Jaljuli, Orestis Panagiotou and Ruth Heller.

    Dr. Yoav Benjamini

    Yoav Benjamini is Professor Emeritus of Applied Statistics at the Department of Statistics and Operations Research at Tel Aviv University, and a member of the Sagol School of Neuroscience and the Edmond Safra Bioinformatics Center. He was a visiting professor at the University of Pennsylvania, University of California, Berkeley, Stanford, and Columbia Universities. Yoav is a co-developer of the widely used False Discovery Rate concept and methodology. His other research topics are replicability and reproducibility in science and data mining, with applications in Biostatistics, Bioinformatics, Animal Behavior, Geography, Meteorology, Brain Imaging and Health Informatics. He is a member of the Israel Academy of Sciences and Humanities and the US National Academy of Sciences, and received the Israel Prize in Statistics and Economics and the Founders of Statistics Prize of the International Statistical Institute.

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • May
    5
    11:00am - 2:00pm

    Earth Lab GIS Conference 2022

    Institute at Brown for Environment & Society (IBES)

    The DEEPS Introduction to Geographic Information Systems Class is hosting a special “mini-conference” poster session. Join us in viewing the students’ projects highlighting their skills in geospatial analysis. Light refreshments will be provided. 

    Physical & Earth Sciences
  • Patching Development
    May
    2
    4:00pm - 6:00pm

    Rajesh Veeraraghavan Book Adda — Patching Development: Information Politics and Social Change in India

    Watson Institute for International and Public Affairs

    Commentators:
    Rachel Brulé,
    Boston University
    Zehra Hashmi, Brown University
    Robert Jenkins
    , Hunter College, CUNY
    Vijayendra Rao, The World Bank

    Rajesh Veeraraghavan is an Assistant Professor of Science Technology and International Affairs (STIA) Program at Georgetown University’s School of Foreign Service. His work focuses on the intersection of data, technology and governance. I am interested in the politics of data and technology, inequality and the role of data and technology to improve lives of the marginalized. Previously, Veeraraghavan was a postdoctoral fellow at the Watson Institute of International and Public Affairs at Brown University and was previously a Fellow at the Berkman Center at Harvard University. He consulted for the Gates Foundation and Open Society Foundation.

    About the Book:
    How can development programs deliver benefits to marginalized citizens in ways that expand their rights and freedoms? Political will and good policy design are critical but often insufficient due to resistance from entrenched local power systems. InPatching Development, Rajesh Veeraraghavan presents an ethnography of one of the largest development programs in the world, the Indian National Rural Employment Guarantee Act (NREGA), and examines NREGA’s implementation in the South Indian state of Andhra Pradesh. He finds that the local system of power is extremely difficult to transform, not because of inertia, but because of coercive counter strategy from actors at the last mile and their ability to exploit information asymmetries. Upper-level NREGA bureaucrats in Andhra Pradesh do not possess the capacity to change the power axis through direct confrontation with local elites, but instead have relied on a continuous series of responses that react to local implementation and information, a process of patching development. “Patching development” is a top-down, fine-grained, iterative socio-technical process that makes local information about implementation visible through technology and enlists participation from marginalized citizens through social audits. These processes are neither neat nor orderly and have led to a contentious sphere where the exercise of power over documents, institutions and technology is intricate, fluid and highly situated. A highly original account with global significance, this book casts new light on the challenges and benefits of using information and technology in novel ways to implement development programs.

  • Brown University and Lifespan junior faculty, postdocs, residents, medical students, and graduate students – Do you have an idea for a digital health innovation that will help solve a sticky public health challenge?

    Apply to participate in the first ever Digital Health Pitch Competition! This is a program that encourages digital health innovation and rewards brilliant ideas with seed funding and mentorship. Complete the application by May 2, 2022 to be invited to pitch your digital health innovation. Your team could walk away with up to $25,000 in prize money!

    Applications open April 15, 2022.

    Biology, Medicine, Public Health, Entrepreneurship, Graduate School, Postgraduate Education, Psychology & Cognitive Sciences, Social Sciences
  • Join Advance-CTR for the second of two training modules on the ins and outs of NVivo and how to utilize the software for your study. The Advance-CTR Virtual NVivo Modules will be conducted on a PC and provide an introduction to the NVIV0 2020 software version. Mac specific training will take place at a future date.

    Module 2: NVivo Project Set-up
    Step-by-step demonstration on how to set up a new project, enter data, create codes, and conduct preliminary analysis.
    Friday, April 29, 2022
    1:00 p.m. - 2:00 p.m. (Open Q&A from 2:00 to 2:30 p.m.)

    There is limited space for these modules and registration will take place on a first-come first serve basis. You will receive confirmation of your spot by Monday, April 18, 2022.
    Biology, Medicine, Public Health, Training, Professional Development
  • Apr
    28
    2:00pm - 3:00pm

    Carney Career Chat with Senior Patent Agent Colleen McKiernan, Ph.D.

    Carney Institute, 4th floor, 164 Angell Street

    Please join the Carney Institute for a conversation with Colleen McKiernan, Ph.D., about her journey from earning a Ph.D. in Cell and Molecular Biology to working as a Senior Patent Agent at Intellia Therapeutics. Colleen will discuss what it’s like to work in patents and intellectual property with a background in science. 

    Biology, Medicine, Public Health, Careers, Recruiting, Internships, Graduate School, Postgraduate Education, Psychology & Cognitive Sciences, Research, Training, Professional Development
  • Apr
    28
    12:00pm

    DSI DUG Initial Brainstorming Session

    164 Angell Street

     

    Calling all concentrators! Thinking about data science but not sure where to get started?

    The DSI is starting a new DUG! If you are an undergraduate student from ANY concentration, interested in data science or the Data Fluency Certificate, we invite you to join us for an initial interest/brainstorming session!

    Please come prepared to share your thoughts about what a DS DUG can do for you! Pizza and refreshments will be served. 

    Please register in advance!

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Humanities, Identity, Culture, Inclusion, Mathematics, Technology, Engineering, Physical & Earth Sciences, Psychology & Cognitive Sciences, Social Sciences, Student Clubs, Organizations & Activities
  •  

    Ozge Whiting, Ph.D.

    Vice President of Data and Machine Learning, Cellino

     

    AI-guided Induced Pluripotent Stem Cell-based Therapy Manufacturing at Cellino

    Cellino is on a mission to make personalized, autologous cell therapies accessible to patients. Stem cell-derived regenerative medicines are poised to cure some of the toughest diseases within this decade, including Parkinson’s, diabetes, and heart disease. Patient-specific cells provide the safest, most effective cures for these indications. However, current autologous processes are not scalable due to extensive manual handling, high variability, and expensive facility overhead. Cellino’s vision is to make personalized regenerative medicines viable at a large scale for the first time. Cellino’s ML team is building the AI engine that monitors the cells as they grow and guides the laser-based editing system to eliminate manual review processes. 

     

    This talk is hosted by Andras Zsom, Assistant Professor of the Practice of Data Science; Director of Industry and Research Engagement; Director of Graduate Studies, Data Science Initiative.

    Refreshments and snacks will be provided.

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering
  • Todd Golde, M.D., Ph.D.

    Professor, Department of Neuroscience
    Director, McKnight Brain Institute
    University of Florida
     


    Please note that this is a hybrid seminar, in Marcuvitz Auditorium in Sidney Frank Hall and through Zoom (please email [email protected] for the link information).

    Biology, Medicine, Public Health, Research
  • Join Advance-CTR for the first of two training modules on the ins and outs of NVivo and how to utilize the software for your study. The Advance-CTR Virtual NVivo Modules will be conducted on a PC and provide an introduction to the NVIV0 2020 software version. Mac specific training will take place at a future date.

    Module 1: Introduction and Overview to the NVivo Software
    A general overview and introduction on the NVivo software and its potential uses.
    Friday, April 22, 2022
    1:00 p.m. - 2:00 p.m. (Open Q&A from 2:00 to 2:30 p.m.)

    There is limited space for these modules and registration will take place on a first-come first serve basis. You will receive confirmation of your spot by Monday, April 18, 2022.
    Biology, Medicine, Public Health, Training, Professional Development
  • Patrick Rashleigh
    Apr
    22
    12:00pm - 1:00pm

    Data Visualization Seminar

    Zoom

    Join Advance-CTR, S4, and the Brown Library for the first of this 2-part series exploring data visualization, its methodology, and application in biomedicine and health. The purpose of this series is to serve as a data visualization introduction for clinicians and others who may be interested in using these tools and methods in their research. 

    Friday, April 22, 2022 12:00 p.m. - 1:00 p.m.
    “Data Visualization from 10,000 feet: A Quick Introduction to Visual Communication”
    Featuring E. Patrick Rashleigh
    The Center for Digital Scholarship, Brown University Library

    Poised to plunge into data visualization, making the latest-and-greatest fancy interactive extravaganzas? Well, hang on—before pulling out all the tools, let’s take a step back and think about some basic principles of visual perception, design and representation, and communicating to an audience.

    Biology, Medicine, Public Health, Research, Teaching & Learning
  • Apr
    22

    Biostatistics Doctoral Candidate Bing Li

    Generalizing the Area Under the ROC Curve to a New Target Population

    Please join us as Biostatistics doctoral candidate Bing Li defends her thesis, “Generalizing the Area Under the ROC Curve to a New Target Population.”

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Graduate School, Postgraduate Education
  •  

    Elizabeth Fussell, Ph.D.

    Professor of Population Studies and Environment and Society, Population Studies and Training Center, Brown University

     

    DATASET ON ENVIRONMENT AND MIGRATION SYSTEMS FOR THE US

    Climate-related hazards are expected to permanantly or temporarily displace millions of people around the world in the coming decades, including the US. A coupled human-environment understanding of the hazard-migration relationship anchored in the social sciences is critical for policty development around post-disaster rebuilding and relocation, and anticipatory managed retreat. In this presentation, Dr. Fussell will describe an effort to construct and validate a new spatiotemporal dataset – the Dataset on Envrionment and Migration Systems (DEMS) – that uses linked data records from the US Census Bureau to support analyses of environmental hazards and internal migration, and to use the DEMS to test hypotheses derived from migration systems and social vulnerability approaches that investigate the relationship between hazards and migration. The construction of DEMS benefits from Census Bureau data linkage infrastructure newly accessible to external researchers. The presentation will focus on the approach to constructing the DEMS and the goals of the project which was recently funded by the National Science Foundation.

    This talk is based on an NSF-funded research project that began in early 2022.

     

    Elizabeth Fussell, Ph.D. is a sociologist and demographer whose research focuses on societal and environmental causes of migration and population change. She joined Brown University and the PSTC in 2014 and is also affiliated with the Institute at Brown for Environment and Society. Dr. Fussell is also Editor-in-Chief of the Springer Journal, Population and Environment. Her research has been supported by the National Institutes of Health, the MacArthur Foundation, and the Russell Sage Foundation. Dr. Fussell is an author of the Fifth National Climate Assessment’s Chapter on Human Social Systems.

  • This session will cover a new framework for advancing digital health equity, and then go through multiple case studies that highlight practical research and implementation approaches within this space – focusing on the safety net healthcare system within San Francisco.

    Speaker Bio

    Courtney Lyles, PhD, is an Associate Professor in the Division of General Internal Medicine at Zuckerberg San Francisco General Hospital, the Center for Vulnerable Populations, and the Department of Epidemiology and Biostatistics at the University of California, San Francisco. A trained health services researcher, she uses quantitative and qualitative methods to examine quality of care, health behavior, and health outcomes. She is also an Associate Director of the UCSF program Implementation Science program based in the Department of Epidemiology and Biostatistics. Finally, she holds an affiliate investigator appointment at the Kaiser Permanente Northern California Division of Research.

    Her research specifically focuses on harnessing health information technology to improve patient-provider communication for chronic disease self-management to ultimately reduce disparities in health and healthcare outcomes for low-income and racial/ethnic minority populations. She currently is co-PI of an R01 from the Agency for Healthcare Research and Quality to use machine learning to send personalized text messages to patients to motivate physical activity, including leveraging user-centered design and implementation science principles.

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

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Training, Professional Development
  •  

    Please join the Data Science Initiative for a discussion and information session with Brown Technology Innovations:
    “Intellectual Property Protection and Commercialization, and an Upcoming Innovation Fellows Program for Graduate Students and Postdoctoral Researchers at Brown University”

    Tuesday, April 19 from 2:00 PM to 3:00 pm

    Please register in advance!

  • International Festival poster with Globe, participating student organizations and event details
    Apr
    16
    12:00pm - 3:00pm

    International Festival

    Main Green

    From Global to Local: Telling Stories of Home

    Join us for International Festival, one of the largest events hosted by the Global Brown Center for International Students! International Festival is a day of celebration of Brown’s vibrant global community, bringing together student organizations and University offices that uplift and enrich the global experience here at Brown. From 12-3pm on Saturday, April 16th, come to the main green to enjoy delicious food, games, and performances! From the cultural organizations serving appetizers and desserts from home, to performance organizations putting on an incredible show, celebrate a diverse set of stories from around the world in many different forms. Participants will be able to buy tickets to purchase food and other items on the day of the event. Please note that this event is cash-only.

    This year, the International Festival is co-hosted by Storytellers @ Brown and at each cultural organization booth, we’re featuring a work of literature that the clubs have chosen to represent their group! Get amazing book recommendations and listen to performers from Storytellers and beyond share their personal narratives in a global context. Expect performances from Storytellers, Brown Bhairavi, Brown Lion Dance, Mezcla, and more! 

    Special thanks to participating student organizations and University offices:

    1. AfriSA
    2. Brasa @ Brown
    3. Brown/RISD Arab Society
    4. Brown Taiwan Society
    5. Brown UNICEF
    6. Center for Language Studies
    7. Chinese Student Association
    8. Filipino Alliance (FA)
    9. Global Brown
    10. Global Health Initiative
    11. Hawaiʻi at Brown
    12. Hellenic Students Association
    13. Himalayan Cultural Association
    14. Japanese Cultural Association
    15. Latinx Student Union
    16. LGBTQ Center
    17. Nigerian Students Association
    18. Office of International Programs
    19. Project Access
    20. Sarah Doyle Center for Women and Gender
    21. South Asian Students’ Association
    22. Storytellers @ Brown
    23. Vietnamese Student’s Association
  • revised arcade graphic
    Apr
    15
    6:00pm

    U-FLi Arcade!

    Sciences Library

    The U-FLi Center is so excited to welcome you all to the U-FLi Arcade, a whole new way to come into community with one another! Spend one night in the U-FLi Arcade and win prizes! Play great games, like Super Smash Bros, Mario Party, Mario Kart– and classic games like UNO and Spoons! Spend some time getting to know your U-FLi peers, making friends, and having fun with your chosen family.

    Space is limited to please make sure to RSVP.

    Food will be provided!

    Identity, Culture, Inclusion
  •  

    This will be a hybrid talk. In-person attendance is encouraged. 

     

    IRENE KAPLOW

    Postdoctoral Researcher, Carnegie Mellon University

     

    Relating Enhancer Genetic Variation Across Mammals to Complex Phenotypes Using Machine Learning

    Advances in genome sequencing have provided a comprehensive view of cross-species conservation across small segments of nucleotides. These conservation measures have proven invaluable for associating phenotypic variation, both within and across species, to variation in genotype at protein-coding genes or very highly conserved enhancers. However, these approaches cannot be applied to the vast majority of enhancers, where the conservation levels of individual nucleotides are often low even when enhancer function is conserved and where activity is tissue- or cell-type-specific. To overcome these limitations, we developed the TACIT (Tissue-Aware Conservation Inference Toolkit) approach, in which convolutional neural network models learn the regulatory code connecting genome sequence to open chromatin in a tissue of interest, allowing us to accurately predict cases where differences in genotype are associated with differences in open chromatin in that tissue at enhancer regions. We established a new set of evaluation criteria for machine learning models developed for this task and used these criteria to compare our models to models trained using different negative sets and to conservation scores. We then developed a framework for connecting these predictions to phenotypes in a way that accounts for the phylogenetic tree. When applying our framework to the motor cortex and parvalbumin neurons, we identified dozens of new enhancers associated with the evolution of brain size and vocal learning.

     

    Learn more about Irene Kaplow…

  • Event Poster
    Apr
    13
    5:00pm - 7:00pm

    Digital Healthcare: From Cyber Threat to Health Equity

    List Art Building

    Join the Office of the Vice President of Research (OVPR) for a special event featuring Eric Perakslis and Dr. Megan Ranney.

    About this event

    While digital health offers many clear promises and opportunities, the complexities are real and oftentimes foreign to traditional healthcare. Deriving value for patients, clinicians and institutions requires mixing technology, medicine, basic science and the internet, the clinic environment and the home environment, and much more. In this talk, Erik Perakslis and Dr. Megan Ranney, Academic Dean of the School of Public Health, will discuss the 10 emerging “toxicities” of digital health and how they can be mitigated and managed with a specific focus on health equity.

    This event is cosponsored by OVPR, the Brown-Lifespan Center for Digital Health and the Brown Data Science Initiative.

    This event is free and open to the public. Registration is required.

    Closed Captioning will be provided.

    Speaker Bios

    Eric Perakslis is the chief science and digital officer at the Duke Clinical Research Institute and professor of population health sciences and chief technology strategist at the Duke University School of Medicine. Previously, Perakslis was a Rubenstein Fellow at Duke University, where his work focused on collaborative efforts in data science that spanned medicine, policy, engineering, computer science, information technology and security.

    Prior to Duke, Perakslis served as chief scientific advisor at Datavant, lecturer in the Department of Biomedical Informatics at Harvard Medical School and strategic innovation advisor to Médecins Sans Frontières, as well as in roles with the U.S. Food and Drug Administration, Johnson & Johnson Pharmaceuticals and ArQule Inc.

    Perakslis has a Ph.D. in chemical and biochemical engineering from Drexel University. He also holds BSChE and M.S. degrees in chemical engineering.

    Megan Ranney is a practicing emergency physician, researcher and advocate for innovative approaches to health. Her work focuses on the intersection between digital health, violence prevention and population health.

    Ranney is the founding director of the Brown-Lifespan Center for Digital Health, the academic dean for the Brown University School of Public Health and the Warren Alpert Endowed Associate Professor in the Department of Emergency Medicine at Rhode Island Hospital/Warren Alpert Medical School of Brown University. She is an editor for the journal Annals of Emergency Medicine and a Fellow of the American College of Emergency Physicians.

    Ranney has a B.S. in history of science from Harvard University, an M.D. from Columbia University College of Physicians and Surgeons in New York City and an MPH from Brown University.

    Sponsoring Departments

    The Office of the Vice President for Research (OVPR) at Brown University strategically collaborates with internal and external stakeholders to accelerate the global impact of Brown University’s research and scholarship.

    Brown’s innovative research drives broad, positive change across the globe. The OVPR team provides the expertise needed to uphold the high, ethical standard that is the hallmark of research at Brown. The OVPR staff are flexible and anticipate the needs of Brown researchers, and serve as key partners to the non-profit, government and industry sectors. As a result, Brown researchers engage in successful collaborations within and beyond the University.

    To learn more: www.brown.edu/research

    The Data Science Initiative at Brown University is a hub for research and education in the foundational methodologies, domain applications, and societal impacts of data science.

    To learn more: www.brown.edu/initiatives/data-science

    The Center for Digital Health is a hub where creative minds from Brown and its affiliated hospital partners collaboratively design, test, and deploy digital solutions to society’s most pressing health challenges.

    To learn more: https://digitalhealth.med.brown.edu/

  • Apr
    13
    12:00pm - 1:00pm

    Data Science in Industry - Biostatistics Alumni Talk

    121 South Main Street

    Welcome to the Spring 2022 series of Career Information sessions where we will feature Biostatistics alumni as well as representatives from prestigious companies in the local area. Current students will have the opportunity to learn more about health-related and other job opportunities for people with a solid background in biostatistics, data analysis, and quantitative methods, as well as tips for an effective job search, and advice on pursuing a successful career after graduation.

    The speakers for this event will be:

    Isaac Zhao, Sc.M.
    Master of Science, 2019

    Biostatistician/Data Scientist at Alkermes (Contractor)
    Doctoral Student in Data Science at Worcester Polytechnic Institute

    Julia Roberta, Sc.M.
    Master of Science, 2019
    Senior Biostatistician at Exact Sciences

    Lunch will be served!

    Biology, Medicine, Public Health, Careers, Recruiting, Internships, Graduate School, Postgraduate Education
  • Join the Carney Institute for Brain Science for a lively conversation about health disparities in brain-related disorders, featuring:

    • Monica Rivera-Mindt, Ph.D., president of the Hispanic Neuropsychological Society, a professor of psychology at Fordham University and a board-certified neuropsychologist. Rivera-Mindt’s research focuses on the intersection between cultural neuroscience, neuropsychology and health disparities utilizing a novel community-based approach.
    • Diana Grigsby, Ph.D., an associate professor of behavioral and social sciences and of epidemiology at Brown University. Grigsby’s research seeks to capture complex processes in the food, social and built environments to facilitate a better understanding of their influence on what has been coined the three pillars of health: diet, physical activity and sleep.

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

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research
  • Mar
    28
    12:00pm - 1:00pm

    Statistics Seminar | Despina Kontos, Ph.D.

    121 South Main Street

    Dr. Despina Kontos, Matthew J. Wilson Associate Professor of Research Radiology II, Associate Vice-Chair for Research, and Director of the Computational Biomarker Imaging Group (CBIG) in the Center for Biomedical Image Computing and Analytics (CBICA) at the Radiology Department of the University of Pennsylvania

    The Role of Imaging as a Biomarker in Integrated Precision Diagnostics for Cancer Care

    As new options for breast cancer screening, early detection and treatment become available it is essential to provide accurate, clinically relevant methods to identify women that would benefit most from specific approaches. An emerging approach to improve individualized risk assessment in clinical decision making for breast cancer is the incorporation imaging biomarkers. Our studies with multi-modality breast imaging suggest that imaging can play an important role for personalizing patient care. Quantitative measures of breast density and parenchymal texture can improve the prediction accuracy of breast cancer risk estimation models and potentially, help guide personalized breast cancer screening protocols. Tumor phenotypic characteristics, such as shape, morphology, and heterogeneity of contrast enhancement kinetics from magnetic resonance imaging are indicative of molecular subtypes of breast cancer and correlate with the probability of future recurrence. Such phenotypic tumor imaging markers can also be used as surrogates for treatment response, including neo-adjuvant chemotherapy, and help identify earlier patients that are most likely to respond to treatment. This emerging evidence therefore suggests a new clinical paradigm that will necessitate integrating multi-modality imaging biomarkers with genomics, histopathology, and clinical risk factors to assess individualized patient risk and help better guide clinical decisions for breast cancer. This talk will provide an overview of investigations currently on-going at our institution that include digital mammography, digital breast tomosynthesis and magnetic resonance imaging biomarkers and their potential clinical utility in guiding personalized screening, prevention, and treatment approaches for breast cancer.

    Dr. Despina Kontos Ph.D., is the Matthew J. Wilson Associate Professor of Research Radiology II, Associate Vice-Chair for Research, and Director of the Computational Biomarker Imaging Group (CBIG) in the Center for Biomedical Image Computing and Analytics (CBICA) at the Radiology Department of the University of Pennsylvania. Dr. Kontos received her C.Eng. diploma in Computer Engineering and Informatics from the University of Patras in Greece and her Ph.D. degrees in Computer Science from Temple University in Philadelphia. Her research interests focus on investigating the role of quantitative imaging as a predictive biomarker for guiding personalized clinical decisions in breast cancer screening, prognosis and treatment. She has been the recipient of the ECOG-ACRIN Young Investigator Award of Distinction for Translational Research and is currently leading several on-going research studies, funded both by the NIH/NCI and private foundations, to incorporate novel quantitative multi-modality imaging measures of breast tumor and normal tissue composition into cancer risk prediction models.

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • THIS EVENT HAS BEEN POSTPONED UNTIL THE FALL 22 SEMESTER.

    Unfortunately, due to some unforeseen circumstances, Chelsea Manning will not be able to be at Brown University – all events scheduled for Wednesday and Thursday are thereby canceled. We are excited, however, to host Chelsea early in the Fall semester! We will disseminate the dates of her visit in the coming months.

    We deeply apologize for this schedule change.

    Alberto Bortoni, co-president.

    Brown War Watch

     

    CHELSEA MANNING: The Future of Privacy and Data

    Manning delivers a keynote address on the importance of the human element in the development of technology and the imperative to operate with responsibility, accountability, and a strong moral compass, for the sake of not only our privacy but for the future of humanity, which may very well depend on it. 

    Tickets can be reserved through Eventbrite.

    This talk is free and open to the public. 

     

    Presented by Brown War Watch and co-sponsored by the Data Science Initiative.

    This talk is also in partnership with the Watson Institute for International and Public Affairs, the History Department, the Math Department, the COGUT Institute for the Humanities, oSTEM, the Pembroke Center for Teaching and Research on Women, and the Political Theory Project at Brown University.

    Education, Teaching, Instruction, Faculty Governance, Government, Public & International Affairs, Humanities, Identity, Culture, Inclusion, International, Global Engagement, Mathematics, Technology, Engineering, Research
  • In this Workshop, participants will have the chance to learn the tools needed to build an effective pitch deck and put those tools to use by building a pitch deck of their own. Participants will be able to access NEMICs pitch template to build a framework for their business and strategic plans as they move forward through development.

    Speaker Bio

    Aidan Petrie is a founding Partner of NEMIC (New England Medical Innovation Center), New England’s Med Tech venture studio and a Fellow of the Provost at RISD, focused on intersections between user centered design and healthcare. He is a member of Cherrystone Angels and a mentor and adviser to many startups. He also co-founded the MagpieX MedTech Accelerator Fund. MagpieX provides pre-seed capital investment, along with clinical and regulatory expertise to Med Tech and Digital Health startups, through a synergistic partnership with NEMIC. Aidan’s passion for innovation and design has helped bring hundreds of products to market that range from simple drug compliance aids to wearable therapeutics, home monitoring products and complex surgical systems. Aidan was the founding partner of Ximedica, one of the world’s leading medical device developers. Ximedica integrates regulated devices with connected systems to provide the next generation of healthcare solutions. Aidan remains the Chief Innovation Officer Emeritus and holds over 100 patents.
    —–
    Please note that this virtual event, including attendees’ Zoom video, audio and screen name, and questions or chats, will be recorded. All or portions of the event recording may be shared through the Center for Digital Health’s digital channels. Individuals who do not want their identities to be captured are solely responsible for turning off their camera, muting their microphone and/or adjusting their screen name accordingly. By attending this event, you consent to your name, voice, and/or image being recorded and to CDH reproducing, distributing and otherwise displaying the recording, within its sole discretion.

    The CDH is committed to providing universal access to all of our events. Please contact Allison Seeley ([email protected]) to request disability accommodations. Advance notice is necessary to arrange for some accessibility needs.
    Biology, Medicine, Public Health, Entrepreneurship, Training, Professional Development
  •  

    NIKOS TAPINOS, MD, Ph.D.

    Brown University


    CANCER STEM CELL PLASTICITY: INTEGRATING COMPUTATIONAL BIOLOGY WITH THE BEDSIDE

    Dr. Nikos Tapinos will present the concept of cancer stem cell plasticity and why this is crucial for understanding the evolution of cancer and therapeutic resistance. He will present computational; biology projects that help discover molecular mechanisms that define cellular plasticity and finally, Dr. Tapinos will show examples of how this new information can be used for the benefit of cancer patients. 

     

    Learn more about Dr. Nikos Tapinos…

  • Mar
    16
    1:00pm - 1:50pm

    Critical Computing Speaker Series: William Lockett

    Granoff Center for the Creative Arts

    The Critical Computing Speaker Series presents William Lockett

    “Media Laboratory Classrooms for Human Model Organisms, 1952–1974”

    This presentation provides to the Digital Media students a way into the scientific and philosophical stakes of model mindsas they relate specifically to the pre-history of the personal computer. I show that model builders used modern logic and sensory deprivation architectures to transform classrooms into laboratory contexts designed for studies of the development in children of numerical and linguistic abilities. I argue that this background of “model work”—behind the foreground of networked personal devices—stabilizes a set of philosophical stakes that can guide the formation of a critical media history of computing in the present.

    Please be sure to RSVP using the form below.

    Arts, Performance, History, Cultural Studies, Languages, Humanities, Mathematics, Technology, Engineering, Social Sciences
  • Join Founder of OCTO Product Development, Justin Sirotin in a conversation about the value and importance of MVP (minimum viable product) exploration and definition, how to ensure feasibility/desirability & viability, and the processes to define these.

    Speaker Bio

    Justin Sirotin is an entrepreneur, design strategist, and expert in product development and innovation. He has spent his 25+ year career developing successful products, services, brand experiences and businesses for multinational brands and start-ups alike. He is the founder of three successful companies delivering high-end consumer goods and a product strategy, research, design, development, and engineering services firm. He is currently taking on his most ambitious plan to date, a fourth company developing a complex IoT software platform that will change the way data is collected and distributed. This wide range of experiences gives Justin a unique perspective on driving innovation, regardless of company size or industry vertical. Justin has developed a unique point of view on the complete process for creating products through his work with clients including Bose, Google, Hexagon, Schneider Electric, Leica Geosystems, Johnson and Johnson, Titleist, and CVS. This background led him to start OCTO Product Development, a product development firm that delivers highly individualized software and product design and engineering services to companies of all sizes.

    Before launching OCTO Product Development, Mars Made, and Route Werks, Justin worked as the General Manager for Item New Product Development (now Ximedica), a medical technologies product development firm headquartered in Providence, RI. In addition, Justin has been an adjunct faculty member at RISD since 2008 in the Industrial Design Department.

    ———-

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

    The CDH is committed to providing universal access to all of our events. Please contact Allison Seeley ([email protected]) to request disability accommodations. Advance notice is necessary to arrange for some accessibility needs.

    Biology, Medicine, Public Health, Entrepreneurship, Training, Professional Development
  • Dr. Nicholas Petrick, Deputy Director for the Division of Imaging, Diagnostics and Software Reliability at the Center for Devices and Radiological Health, U.S. Food and Drug Administration and member of the FDA Senior Biomedical Research Service

    Current regulatory validation methods for artificial intelligence models applied to medical imaging data

    Statical decision making, artificial intelligence and machine learning (AI/ML) methods have a long history being applied to digital medical image data with mammography computer-aided detection devices approved back in 1998 by FDA and other quantitative tools/measures approved or cleared even earlier. The number of AI/ML tools applied to medical image data remained relatively consistent until a few years ago. The FDA is currently seeing a substantial increase in the number of submitted AI/ML tools because of recent advances in deep learning methods in other commercial areas with the potential for these tools to have a much wider impact on clinical decision-making. Some newer medical AI/ML applications include detection and diagnostic tools to aid in disease detection and assessment, triage tools to aid in prioritizing time-sensitive imaging studies, quantitative measurement tools, structural segmentation tools, image reconstruction or denoising tools, and optimization tools to aid in image acquisition to name a few. In this talk, I will introduce the audience to FDA’s medical device regulatory processes with the goal of demystifying how medical devices are regulated in the U.S. The main focus of my talk will be on the validation methods currently being applied to AI/ML device assessment and a discussion of our ongoing regulatory research developing methods to potentially improve AI/ML algorithm generalizability, robustness analysis as well as AI/ML device performance assessment.

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • This presentation will introduce audiences to market access considerations for digital health innovations.

    Speaker Bio

    Greg Gregory, Ph.D., has a decade of strategy and management consulting experience working with top clients in the pharmaceutical and biotechnology industries and has a passion for rare diseases and orphan drugs. He has over a decade of biomedical research experience from leading research institutions.

    He has 10+ years of experience in Market Access and Commercialization Strategies, Pricing and Reimbursement Strategies, Orphan Drugs, Vaccine, Oncology and Specialty Therapeutics Trade and Distribution Strategies, Hub Services Strategy, LOE, Payer Value Proposition, Vaccine Go-to-Market Strategy, and Digital Therapeutics. His areas of focus include working with rare disease and specialty products as well as gaining experience working across a range of therapeutic areas, including cardiovascular, hematology/oncology, immunology, respiratory, CNS, autoimmune disorders, vaccines, and digital therapeutics. He has delivered and/or managed over 200 consulting engagements for life-science companies ranging from start-up biotechs to top 5 pharmaceutical manufacturers. Greg is experienced in competitive payer dynamics, multi-indication access challenges, and adjunctive/combination therapies within orphan & oncology.

    Greg leverages over a decade of biomedical research experience from leading research institutions. During his time in academia, his PhD and post-doctoral research furthered our understanding of the mechanisms contributing to several human diseases including the autoimmune disease multiple sclerosis and blood disorders such as thalassemia and thrombocytopenia.

    ———-

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

    The CDH is committed to providing universal access to all of our events. Please contact Allison Seeley ([email protected]) to request disability accommodations. Advance notice is necessary to arrange for some accessibility needs.

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

    Statistics C.V. Starr Lecture | Katherine Heller, Ph.D.

    121 South Main Street

    Dr. Katherine Heller, Research Scientist at Google

    Towards Trustworthy Machine Learning in Medicine and the Role of Uncertainty

    As ML is increasingly used in society, we need methods that we have confidence that we can rely on, particularly in the medical domain. In this talk I discuss 3 pieces of work, the role uncertainty plays in understanding and combating issues with generalization and bias, and particular mitigations that we can take into consideration.

    1) Sepsis Watch - I present a Gaussian Process (GP) + Recurrent Neural Network (RNN) model for predicting sepsis infections in Emergency Department patients. I will discuss the benefit of uncertainty given by the GP. I will then discuss the social context in introducing such a system into a hospital setting.

    2) Uncertainty and Electronic Health Records (EHR) - I will discuss Bayesian RNN models developed for mortality prediction, and the distinction between population level predictive performance and individual level predictive performance, and its implications for bias.

    3) Underspecification and the credibility implications of hyperparameter choices in ML models – I will discuss medical imaging applications and how using the uncertainty of model performance conditioned on choice of hyperparameters can help identify situations in which methods may not generalize well outside the training domain.

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • This seminar will present an overview of the regulatory landscape in the United States as well as important considerations for digital health innovations to seek and achieve FDA approval.

    Speaker Bio

    Dr. Shrawan Patel, MD is a recognized voice in the digital health ecosystem with expertise ranging from digital therapeutics and successful patient engagement techniques, through to digital infrastructure design for pharmaceutical Medical Affairs teams. With a clinical background that includes Internal Medicine and General/Colorectal Surgery, Shrawan brings a wealth of clinical experience to the table.

    Shrawan has a particular interest in the application of Behavioral Economics in healthcare with proven outcomes that exceed the industry norm and have been recognized by national associations including the American College of Radiology, American Gastroenterology Association, and the American Medical Informatics Association. Shrawan speaks regularly around the country on innovation and digital health.

    ———-

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

    The CDH is committed to providing universal access to all of our events. Please contact Allison Seeley ([email protected]) to request disability accommodations. Advance notice is necessary to arrange for some accessibility needs.

    Biology, Medicine, Public Health, Entrepreneurship, Training, Professional Development
  • This seminar will present an overview of the strategies digital health innovators can employ to protect their intellectual property.

    Speaker Bios

    Dan Holmander, JD

    Daniel is a U.S. Patent and Trademark Office registered patent attorney (U.S. Reg. No. 59,518) and admitted to the federal district courts in Rhode Island and Massachusetts. Most importantly, Daniel has extensive experience in developing and executing strategies for clients’ intellectual property portfolios with a sharply honed focus on furthering their business objectives. As a registered patent attorney with the United States Patent and Trademark Office, Daniel has a passionate focus on assisting a wide range of clients from early stage technology companies to Fortune 500 companies located throughout the United States and Canada with their intellectual property law issues. More specifically, Daniel advocates and counsels clients in intellectual property areas such as patents, trademarks, copyrights, trade secrets, unfair competition, and domain names. Daniel’s range of technology areas is vast including medical technologies, software, biofuels, nanotechnology, material science, eyewear, coatings, vaccines, chemicals, consumer packaging, biotechnology, and green technologies.

    Daniel advises on all aspects of intellectual property protection including prosecution, procurement, federal litigation, proceedings before the Patent and Trial Appeal Board (PTAB) (i.e. reexaminations and interferences), Trademark Trial and Appeals Board (TTAB) (i.e. oppositions, cancellations, appeals), reexamination, interference, Uniform Domain-Name Dispute Resolution Policy (UDRP) proceedings, licensing from technology transfer offices and others, and auctioning of IP. In addition to his law practice, Daniel is an adjunct Professor of Molecular Pharmacology, Physiology, and Biotechnology at Brown University in Providence, RI. In his spare time, Daniel volunteers for non-profit organizations dedicated to children with autism.

    Alex Behrakis, JD

    Alex Behrakis is an intellectual property and corporate attorney with more than nineteen years of experience in law firm and in-house settings. Alex’s practice emphasizes strategic counseling, patent and trademark prosecution, opinions related to infringement and validity, product clearance and freedom-to-operate studies, advising engineering teams on designing around intellectual property rights of third parties, due diligence studies, licensing, and development and management of patent and trademark portfolios, and related litigation. Alex counsels domestic and foreign clients ranging from individual inventors and emerging startup companies to large multinational corporations.

    Alex has significant experience with a wide range of technologies including computer & communication networks, cloud computing, computer software, cybersecurity, artificial intelligence, machine learning, IoT, robotics, data storage systems, electro-optics and lasers, signal processing, control systems, e-commerce, digital & analog electronics, hybrid microelectronics, machine vision, biomedical devices (e.g., artificial implantable heart, mesh stents, drug-eluting stents, photo ablation and cardio ablation systems), and consumer products.

    In addition, Alex has extensive experience advising clients on business matters and transactions including business formation, joint ventures, complex technology agreements, licensing, establishing guidelines and corporate policies, website content and ecommerce, website privacy policies and terms-of-use, advertising and packaging claims, product safety, FTC regulations including COPPA. Alex has negotiated and drafted a variety of agreements including licenses, end-user license agreements, music licenses, joint development agreements, operating agreements, asset purchase agreements, service agreements, distribution agreements, sales representative agreements, manufacturing agreements, settlement agreements, cease and desist letters, confidentiality agreements, and amendments to contracts.

    Prior to entering the legal field, Alex worked as an electrical engineer and software engineer and has more than fifteen years engineering experience involving project management, research and development, hardware and software design, real-time embedded systems, system integration, advanced sensor systems, development of software tools and utilities, and data analysis.

    ———-

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

    The CDH is committed to providing universal access to all of our events. Please contact Allison Seeley ([email protected]) to request disability accommodations. Advance notice is necessary to arrange for some accessibility needs.

    Biology, Medicine, Public Health, Entrepreneurship, Training, Professional Development
  • Brown University and Lifespan junior faculty, postdocs, residents, medical students, and graduate students – Do you have an idea for a digital health innovation that will help solve a sticky public health challenge?

    Participate in the first ever Digital Health Pitch Competition! This is a program that encourages digital health innovation and rewards brilliant ideas with seed funding and mentorship. Complete the interest form and gain access to a network of innovators, mentors, and advisors, and be eligible to apply for the Digital Health Pitch Competition where your team could walk away with up to $25,000 in prize money.

    Biology, Medicine, Public Health, Entrepreneurship, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research
  • Feb
    24
    1:00pm

    DSI’s Fair February: Sara Ahmadian, Ph.D.

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Sara Ahmadian, Ph.D.

    Senior Research Scientist, Google

    Revising Traditional Algorithms and Devising New Algorithms to Factor in Fairness

    From digital assistants to movie recommendations and self-driving cars, machine learning is behind many day-to-day interactions with technology. While learning algorithms are not inherently biased, they may pick up and amplify the bias already present in the training data. Thus a recent line of work has emerged on revising traditional algorithms or devising new algorithms to factor in fairness. In this talk, I focus on adding fairness to clustering which is a fundamental problem in data mining and unsupervised machine learning. We introduce a notion of fairness that focuses on requiring a bounded representation of various groups of a sensitive feature, e.g. race, gender, etc., in each cluster. In clustering, the goal is to organize objects into clusters such that elements in the same clusters are “similar”. There are various ways to express the similarity of objects. In metric settings, we are given a distance measure for the objects, and in a non-metric setting, we are given labels in the form of for pairs of objects which identify whether two objects are similar (label +) or not (label -). We look at a fair k-center for the metric case and fair correlation clustering for the non-metric case. If time permits, I will talk about fairness in non-flat clustering, e.g., hierarchical clustering, and how the algorithms for such problems can be modified to accommodate fairness constraints.

    Biography

    Sara Ahmadian is a Senior Research Scientist in the Large-Scale Optimization research team, which is part of the broader NYC Algorithms and Optimization team at Google. Sara earned degrees in Combinatorics and Optimization (M.M. 2010, Ph.D. 2017) from the University of Waterloo, where she was advised by Chaitanya Swamy and supported by an NSERC Fellowship. Sara is a recipient of the 2017 University of Waterloo Outstanding Achievement in Graduate Studies (Ph.D.) designation for her Ph.D. thesis. She worked as a Software Developer for a start-up company in Waterloo after completing her Masters’s and before starting her Ph.D. Prior to that, she earned her BSc in Computer Engineering at Sharif University of Technology (Iran). Her research interests include diverse and fair sampling, data summarization, approximation algorithm, design and analysis of algorithms.

  • Feb
    24
    11:00am

    DSI’s Fair February: Cyrus Cousins, Ph.D.

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Cyrus Cousins, Ph.D.

    Visiting Assistant Professor, Computer Science, Brown University

    Data Categorization and Welfare Outcomes in Machine Learning

    Data-quality issues often compound the unfairness, as majority groups are often well-studied, with copious high-quality data available, while marginalized or minority groups are understudied, and available data lack in quality. This work operates in the setting wherein only partial information is available on protected group membership. In particular, here data are triplets (x, y, z) ∈ (X × Y × Z), where Z is a finite space of g protected groups. Given m training points, we observe covariates x, and labels y, but not group identities zm. Instead, we are given a feasible set Z of group labelings. The task is then to perform (group-dependent) fair learning, with rigorous statistical guarantees. We show that learning approximately minimax-optimal egalitarian or utilitarian malware models in this setting is both statistically and computationally efficient. In particular, our bounds depend on how sharply the unknown group-membership labels are constrained, and thus degrade gracefully as less and less partial information about group membership is available. We also discuss methods by which to statistically constrain the feasible set Z of group membership and the fairness implications of generating such constraints.

    Biography

    Cyrus Cousins, Ph.D. is a Visiting Assistant Professor at Brown University where he also recently completed his doctorate. A perennial scholar of probability, Cyrus has worked in many areas ranging from statistical significance questions in data science and machine learning to econometrics and social justice, where he raises and attempts to answer fundamental questions of what it means to share, allocate and learn fairly. His signature is the application of techniques from statistical learning theory to study how quickly and under what conditions various quantities of interest can be estimated from data. He also works in the analysis of randomized algorithms, Markov chain Monte Carlo, statistical data science, and empirical game theory.

  • Feb
    24
    10:30am

    DSI’s Fair February: Lachlan Kermode

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Lachlan Kermode

    Ph.D. Candidate, Modern Culture and Media, Brown University

    Software Research with Forensic Architecture

    Forensic Architecture (FA) is a research agency, based at Goldsmiths, University of London, investigating human rights violations including violence committed by states, police forces, militaries, and corporations. FA works in partnership with institutions across civil society, from grassroots activists to legal teams, to international NGOs and media organizations, to carry out investigations with and on behalf of communities and individuals affected by conflict, police brutality, border regimes, and environmental violence.

    This talk will speak to a selection of investigations conducted at and with Forensic Architecture that leverage skill sets associated with the computer and data sciences— such as machine learning and full-stack development— to indicate how interdisciplinary work might offer a critical way forward.

     

    Biography

    Lachlan Kermode is a Ph.D. Candidate in the Department of Modern Culture and Media at Brown University, and a Research Fellow at the research agency, Forensic Architecture (Goldsmiths, University of London). After receiving an undergraduate degree in Computer Science from Princeton University (2018), he worked for several years as a Software Researcher at Forensic Architecture and then as a Software Engineer, building cloud infrastructure for machine learning models. Kermode has also worked as a Mobile Developer and as a Full Stack Engineer. His current work is concerned with the political potential of open source and open hardware cultures, the history of computer science and software engineering as disciplinary practices, and the implications and impacts of computing as media at large.

  • Feb
    24
    10:00am

    DSI’s Fair February: Jessica Joan Finocchiaro

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Jessica Joan Finocchiaro

    Ph.D. Candidate, Computer Science, University of Colorado-Boulder

    Bridging Mechanism Design and Machine Learning toward Algorithmic Fairness

    Decision-making systems increasingly orchestrate our world: how to intervene on the algorithmic components to build fair and equitable systems is, therefore, a question of utmost importance; one that is substantially complicated by the context-dependent nature of fairness and discrimination. Modern decision-making systems that involve allocating resources or information to people (e.g., school choice, advertising) incorporate machine-learned predictions in their pipelines, raising concerns about potential strategic behavior or constrained allocation, concerns usually tackled in the context of mechanism design. Although both machine learning and mechanism design have developed frameworks for addressing issues of fairness and equity, in some complex decision-making systems, neither framework is individually sufficient. In this paper, we develop the position that building fair decision-making systems requires overcoming these limitations which, we argue, are inherent to each field. Our ultimate objective is to build an encompassing framework that cohesively bridges the individual frameworks of mechanism design and machine learning. We begin to lay the groundwork towards this goal by comparing the perspective each discipline takes on fair decision-making, teasing out the lessons each field has taught and can teach the other, and highlighting application domains that require a strong collaboration between these disciplines.

     

    Biography: Jessica Joan Finocchiaro is a Ph.D. candidate in the CS Theory group at CU Boulder, working with Dr. Rafael Frongillo (and unofficially with Dr. Bo Waggoner as well). In general, my research interests intersect Theoretical Machine Learning, Algorithmic Game Theory, and Computational Economics. In particular, I am interested in decision-making in the midst of uncertainty, how the questions we ask affect what we learn [from people, machine learning algorithms], and how this uncertainty affects people. I typically study these questions through the lens of property elicitation. I was named as a 2019 National Science Foundation Graduate Research Fellow.

  • Lauren Klein
    Feb
    23
    2:00pm

    DSI’s Fair February: Lauren Klein, Ph.D.

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Lauren Klein, Ph.D.

    Assistant Professor, English, Quantitative Theory and Methods, Emory University

    What is Feminist Data Science?

    How is feminist thinking being incorporated into data-driven work? How are scholars in the humanities and social sciences bringing together data science and feminist theory into their research? Drawing from her recent book, Data Feminism (MIT Press), co-authored with Catherine D’Ignazio, Dr. Klein presents a set of principles for doing data science that is informed by the past several decades of intersectional feminist activism and critical thought. In order to illustrate these principles, as well as some of the ways that scholars and designers have begun to put them into action, she will discuss a range of recent projects including several of her own: 1) a thematic analysis of a large corpus of nineteenth-century newspapers that reveal the invisible labor of women newspaper editors; 2) the development of a model of lexical semantic change that, when combined with network analysis, tells a new story about Black activism in the nineteenth-century US; and 3) an interactive book on the history of data visualization that shows how questions of politics have been present in the field since its start. Taken together, these examples demonstrate how feminist thinking can be operationalized into more ethical, intentional, and capacious data practices in the digital humanities, computational social sciences, human-computer interactions, and beyond.

    Biography

    Lauren Klein is a Winship Distinguished Research Professor and Associate Professor in the Departments of English and Quantitative Theory and Methods at Emory University, where she also directs the Digital Humanities Lab. She is the author of An Archive of Taste: Race and Eating in the Early United States (University of Minnesota Press, 2020) and, with Catherine D’Ignazio, Data Feminism (MIT Press, 2020). With Matthew K. Gold, she edits Debates in the Digital Humanities, a hybrid print-digital publication stream that explores debates in the field as they emerge.

  • This event will introduce the National COVID Cohort Collaborative (N3C) to IDeA-CTR members. The N3C aims to unite COVID-19 data, enabling innovative machine learning and statistical analyses that require a large amount of data – more than is available in any given institution. The goal is to enable rapid collaboration among clinicians, researchers, and data scientists to identify treatments, specialize care, and to reduce the overall severity of COVID-19.

     

    Keynote presentation by Dr. Christopher G. Chute, Bloomberg Distinguished Professor of Health Informatics and Professor of Medicine, Internal Medicine, Johns Hopkins University. Dr. Chute is a Co-Principal Investigator of N3C.

     

    The N3C Data Enclave is a secure platform through which harmonized clinical data provided by contributing members are stored. The Enclave includes demographic and clinical characteristics of patients who have been tested for or diagnosed with COVID-19, and further information about the strategies and outcomes of treatments for those suspected or confirmed to have the virus.

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Research, Training, Professional Development
  • Join the Carney Institute for Brain Science, in conjunction with Love Data Week, for a Carney Methods Meetup featuring Ani Eloyan, assistant professor of biostatistics at Brown, who will discuss methods for defining and estimating clinically relevant biomarkers, such as from longitudinal fMRI.

    Carney Methods Meetups are informal gatherings focused on methods for brain science, moderated by Jason Ritt, Carney’s scientific director of quantitative neuroscience. Videos and notes from previous Meetups are available on the Carney Institute website.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research
  • Feb
    17
    1:00pm

    DSI’s Fair February: Neenu Sukumaran, MD

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Neenu Sukumaran, MD

    Internal Medicine Resident, Roger Williams Medical Center

    Electronic Health Portal Usage Among Non-English Speakers and Older Adults

    Electronic Health Portals (EHPs) are valuable resources for patients and healthcare providers. They augment communication in a privacy-protected, healthcare setting. This is crucial in Rheumatology, where patients are managed long term. EHP use has been linked to improved patient outcomes and reduced healthcare costs. Our goal was to identify active users of EHPs from a single community-academic rheumatology practice in Providence, RI, and understand the factors driving individuals to use EHP, or any barriers that may prevent them from using it.

    Biography

    Dr. Sukumaran is a PGY-2 Internal Medicine Resident at Roger Williams Medical Center. She was born and brought up in India and obtained her medical degree from Calicut University. She has worked in India as a General Physician before moving to the US. She has been involved in medical research in neuropsychiatry before beginning her medical residency at Roger Williams Medical Center. Dr. Sukumaran’s area of interest lies in Rheumatology, with a particular interest in rheumatological diseases in women in the reproductive age group.

  • Feb
    17
    11:30am

    DSI’s Fair February: Andrew Huang

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Andrew Huang

    Ph.D. Candidate, Health Services, Policy, and Practice, Brown University

    Misclassifying Race and Ethnicity: Challenges in Using Medicare Data for Health  Disparities Research
  • Feb
    17
    11:00am

    DSI’s Fair February: Chien-Tzu Cheng

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Chien-Tzu Cheng

    Ph.D. Candidate, Economics, Brown University

    From Doctors’ Offices to Homes: Impacts of Home Pregnancy Test Availability

    Home pregnancy tests first became available in the US local drugstores at the end of 1977, providing private, fast, and accurate pregnancy confirmation. Using county-level drugstore availability to approximate the home pregnancy test availability, this talk examines the impacts on fertility rates, early prenatal care, and female education outcomes. In an event study, it was found that significant trend breaks in fertility rates after 1977 among women aged 15-29 who had access to drugstores; the effects are the strongest among women aged 15-19. Evidence suggests that access to abortion services played a part in explaining the trend breaks among this population. No impact was detected on early prenatal care adoption trends. Furthermore, high school dropouts of the cohort which entered high school right after 1977 significantly declined by 6.7% in areas with greater access to abortion providers.

    Biography

    Chien-Tzu’s research interests include labor economics, health economics, family, and children. Her current project examines the impacts of home pregnancy tests available on fertility rates and early prenatal care adoption when this new technology first went on the US market in 1977. Chien-Tzu is from Taiwan. She enjoys traveling, cooking, and language learning. She also enjoys cleaning during times of stress. 

  • Feb
    17
    10:30am

    DSI’s Fair February: Monia Chopra, MD

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Monia Chopra, MD

    Rheumatology Fellow, Rhode Island Hospital and Roger Williams Medical Center

    TITLE TBA
  • Feb
    16
    2:30pm

    DSI’s Fair February: Aditi Bhowmick

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Aditi Bhowmick

    Director, Development Data Lab

    Returns from an Open Data Approach: India, the Pandemic, and Beyond
  • Feb
    16
    2:00pm

    DSI’s Fair February: Aashish Gupta, Ph.D.

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Aashish Gupta, Ph.D.

    Postdoctoral Fellow, Demography, Harvard University

    Social Disadvantage and Life Expectancy in India

    India has one of the most rigid systems of social stratification in the world, yet little is known about how this system has shaped life expectancy in the country. This talk presents evidence from multiple related papers using large-scale survey data that mortality disparities in India are large, persistent, can be observed across the life course, and cannot be explained by differences in economic status between marginalized social groups and privileged social groups. These findings reveal a pressing need for explicitly challenging social inequalities in health in India. 

     

    Biography

    Aashish is a demographer and currently a David E. Bell Fellow at the Harvard Center for Population and Development Studies. He earned his Ph.D. in Demography and Sociology at the University of Pennsylvania and an MA in Development Studies from the Indian Institute of Technology-Madras. His research uses demographic and field methods to examine interrelations between health, environment, and inequality in India. He was awarded the Dorothy Thomas Award by the Population Association of America, and a Civil Registration and Vital Statistics Fellowship by the International Union for the Scientific Study of Population.

  • Dr. Kristian Lum, Senior Staff Machine Learning Researcher at Twitter

    Closer Than They Appear: A Bayesian Perspective on Individual-level Heterogeneity in Risk Assessment

    Risk assessment instruments are used across the criminal justice system to estimate the probability of some future behavior given covariates. The estimated probabilities are then used in making decisions at the individual level. In the past, there has been controversy about whether the probabilities derived from group-level calculations can meaningfully be applied to individuals. Using Bayesian hierarchical models applied to a large longitudinal dataset from the court system in the state of Kentucky, we analyze variation in individual-level probabilities of failing to appear for court and the extent to which it is captured by covariates. We find that individuals within the same risk group vary widely in their probability of the outcome. In practice, this means that allocating individuals to risk groups based on standard approaches to risk assessment, in large part, results in creating distinctions among individuals who are not meaningfully different in terms of their likelihood of the outcome. This is because uncertainty about the probability that any particular individual will fail to appear is large relative to the difference in average probabilities among any reasonable set of risk groups.

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Feb
    10
    1:00pm

    DSI’s Fair February: Isabella Bellezza-Smull

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Isabella Bellezza-Smull

    Ph.D. Candidate, Political Science, Brown University

    Surveillance and the Globalization of Border Control in the 21st Century
  • Feb
    10
    11:30am

    DSI’s Fair February: Hayley Tsukayama

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Hayley Tsukayama

    Legislative Activist, Electronic Frontier Foundation

    Nerd Smarter, Not Harder: Why Policymaking Needs Technologists
  • Feb
    10
    11:00am

    DSI’s Fair February: Lelia Marie Hampton

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Lelia Marie Hampton

    Ph.D. Candidate, Electrical Engineering and Computer Science, MIT

    Artificial Intelligence Safety for Justice
  • Feb
    10
    10:30am

    DSI’s Fair February: Krystal Maughan

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Krystal Maughan

    Ph.D. Candidate, Computer Science, University of Vermont

    Continual Audit of Individual Fairness in Deployed Classifiers via Prediction Sensitivity
  • Feb
    9
    3:00pm

    DSI’s Fair February: Alexandria LeClerc

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Alexandria LeClerc

    Ph.D. Candidate, Computer Science and Engineering, Oregon State University

    Applied Security and Privacy for Social Movements
  • What does love do to our brains? Why do we fall in love? Why do we stay in love, and what makes us fall out of love?

    Valentine’s Day is just around the corner, and to mark the occasion, the Carney Institute is holding a conversation about how the brain is affected by love, featuring two Brown University scientists who study emotion and motivation.

    Debbie Yee, Ph.D. is a postdoctoral fellow in cognitive, linguistic and psychological sciences investigating the neural and computational mechanisms of interactions between motivation/affective processes, cognitive control and value-based decision-making.

     

    Joey Heffner is a Ph.D. candidate in psychology investigating emotions. He received a graduate award in 2020 from the Carney Institute for a project to understand when and how emotions facilitate or impair social decision-making.

     

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

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research
  • Feb
    9
    2:00pm

    DSI’s Fair February: Sucheta Ghoshal, Ph.D.

    164 Angell Street, Providence, RI 02912

     

    Fair February 2022

    Sucheta Ghoshal, Ph.D.

    Assistant Professor, Human-Centered Design and Engineering, University of Washington

    Critical Technology Practice: Raging Whiteness and/as Technology
  • Call for Applications! Apply for the 2022 Advance-CTR Pilot Projects Program. We’re funding five to eight projects for one-year research awards in two categories:

    1. Proposals with a single PI may apply for $37,500 in direct costs.
    2. Proposals involving multi-PIs from different disciplines may apply for up to $75,000 in direct costs.

    About the Pilots

    The Pilot Projects Program brings investigators together from institutions across the state to develop interdisciplinary collaborations that span the translational research spectrum. The program funds a variety of research that addresses Rhode Island’s health challenges and community health priorities.

    Key Dates & Deadlines

    • January 14, 2022: Last day to schedule calls with leadership

    • February 3, 2022: Preliminary applications due

    • April 4, 2022: Invited, full proposals due

    The anticipated performance period is August 1, 2022 to July 31, 2023.

    Application Resources

    Don’t go at it alone. Schedule a call with our program leadership to discuss your questions, read up on the eight elements of a successful preliminary application, get tips for preparing your application, and review two examples from investigators who have successfully applied to the program.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Emoji occupies the same status as the English letter A, or pictorial Chinese symbols within a universal system called Unicode. This code allows common agreement and translation of language between all devices worldwide, and is the common code for All Apple, Android, Twitter, Facebook, Microsoft, and even most Electronic Medical Records today.

    In 2019, theAnatomical Heart EmojiLungs Emoji Emoji were accepted into Unicode and are available on devices worldwide, but today there still is still no liver, kidney, or spine emoji. When we ask about sharp/stabbingKnife emoji, thunderclapLightning bolt emoji, poundingHammer emoji, or fieryFire emoji pain, we are communicating and transmitting meaning from one person to another. Emoji, and thus digital medicine, can help communicate with patients in a modern, inclusive, and accessible way. Come learn about the fight for inclusion and representation for more medical Emoji, and how Emoji, as a pictorial communication method, can serve as a global open-source visual analogue scale for digital information.

    Shuhan He, MD is a dual faculty member in the Department of Emergency Medicine and Lab of Computer Science at Massachusetts General Hospital. He is Director for Digital Growth Strategy at the MGH Center for Innovation in Digital Healthcare (CIDH) and an Instructor of Medicine at Harvard Medical School. He was the author of the Anatomic heart and lung Emoii that are available now on mobile devices worldwide and senior author of a JAMA article entitled Emoji for the Medical Community, describing the importance of Emoji in medicine.

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

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research
  • Dr. Amy Herring PhD, Sara and Charles Ayres Distinguished Professor of Statistical Science and Research Professor of Global Health at Duke University

    Informative Priors for Clustering

    Based on challenges in a large national study of birth defects, we consider a canonical problem in epidemiology of “lumping” versus “splitting” of groups. In many cases, groups may be unknown in advance, adding the additional challenge of determining group or cluster membership. While there is a very rich literature proposing Bayesian approaches for clustering starting with a prior probability distribution on partitions, most approaches assume exchangeability. Even though there have been some proposals to relax the exchangeability assumption, allowing covariate-dependence and partial exchangeability, limited consideration has been given on how to include concrete prior knowledge on the partition itself. For example, we are motivated by an epidemiological application, in which we wish to cluster birth defects into groups and we have prior knowledge of an initial clustering, provided by experts. As a general approach for including such prior knowledge, we propose a Centered Partition (CP) process. Some properties of the CP prior are described, a general algorithm for posterior computation is developed, and we illustrate the methodology through simulation examples and an application to the motivating epidemiology study of birth defects.

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Tamara Broderick, PhD, Associate Professor in the Department of Electrical Engineering and Computer Science at MIT

    An Automatic Finite-Sample Robustness Metric: Can Dropping a
    Little Data Change Conclusions?
    One hopes that data analyses will be used to make beneficial decisions regarding people’s health, finances, and well-being. But the data fed to an analysis may systematically differ from the data where these decisions are ultimately applied. For instance, suppose we analyze data in one country and conclude that microcredit is effective at alleviating poverty; based on this analysis, we decide to distribute microcredit in other locations and in future years. We might then ask: can we trust our conclusion to apply under new conditions? If we found that a very small percentage of the original data was instrumental in determining the original conclusion, we might expect the conclusion to be unstable under new conditions. So we propose a method to assess the sensitivity of data analyses to the removal of a very small fraction of the data set. Analyzing all possible data subsets of a certain size is computationally prohibitive, so we provide an approximation. We call our resulting method the Approximate Maximum Influence Perturbation. Our approximation is automatically computable, theoretically supported, and works for common estimators — including (but not limited to) OLS, IV, GMM, MLE, MAP, and variational Bayes. We show that any non-robustness our metric finds is conclusive. Empirics demonstrate that while some applications are robust, in others the sign of a treatment effect can be changed by dropping less than 0.1% of the data — even in simple models and even when standard errors are small.

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • The biggest threat to the adoption of artificial intelligence (AI) in healthcare is the concern that training algorithms on real world data will encrypt societal, institutional and individual biases, legitimize them and propagate them at scale. At present, the evaluation metric for machine learning in healthcare is accuracy. But just because an algorithm is accurate does not mean it should be implemented. If all that matters is accuracy, then algorithms developed using real-world data will encrypt the biases and prejudice that taint clinical decision-making. In an ideal world, only patient health and disease factors would determine — and guide the prediction of — clinical outcomes. However, studies have repeatedly demonstrated that this is far from the case. Women with heart attacks have worse outcomes when cared for by male cardiologists. Black newborns have better outcomes when their pediatricians are Black. Outcomes from sepsis are worse in hospitals that disproportionately treat minority patients after adjusting for illness severity and other confounders. To prevent AI from encoding social and cultural biases, we would like to predict an outcome if the world were fair, and the quality of care is the same across populations. We need algorithms that are better than humans - less prejudiced and more fair.

    Dr. Leo Celi is the clinical research director and principal research scientist at the MIT Laboratory for Computational Physiology (LCP), and a practicing intensive care unit (ICU) physician at the Beth Israel Deaconess Medical Center (BIDMC). In his work, Leo brings together clinicians and data scientists to support research using data routinely collected in the process of care. His group built and maintains the publicly-available Medical Information Mart for Intensive Care (MIMIC) database and the Philips-MIT eICU Collaborative Research Database, with more than 25,000 users from around the world. In addition, Leo is one of the course directors for HST.936 – global health informatics to improve quality of care, and HST.953 – collaborative data science in medicine, both at MIT. He is an editor of the textbook for each course, both released under an open access license. “Secondary Analysis of Electronic Health Records” has been downloaded more than a million times, and has been translated to Mandarin, Spanish, Korean and Portuguese. He is the inaugural editor of PLOS Digital Health.

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

    Biology, Medicine, Public Health, Entrepreneurship, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research
  • Holistic Design of Time-Dependent PDE Discretizations
    Jan
    10

    The workshop aims to spur a holistic approach to the design of time-dependent PDE discretizations, particularly in terms of developing time integration techniques that are intertwined with spatial discretization techniques, focusing on: generalized ImEx methods, asymptotic-preserving and structure-preserving methods, methods that exploit low-rank dynamics, analysis of order reduction, parallel in time methods, and performant, maintainable, extensible software implementations.

    Recent decades have seen increasing use of first-principles-based simulations via time-dependent partial differential equations (PDE), with applications in astrophysics, climate science, weather prediction, marine science, geosciences, life science research, defense, and more. Growing computational capabilities have augmented the importance of sophisticated high-order and adaptive methods over “naive’” low-order methods. However, there are fundamental challenges to achieving truly high order and full efficiency in space-time that are yet to be overcome.

    Many advances in temporal and spatial discretization methods have been made independently, by employing techniques in which each part can be developed and analyzed in isolation. However, as spatial discretization methods have become more sophisticated, accurate, efficient, and specialized, computational scientists are finding that temporal integration, in particular, the interface between temporal and spatial discretization, is a source of bottlenecks that limit practical applications. As a response, myriad problem-specific time-stepping approaches have been devised in recent years, but with little feedback to or from the time integration community. This isolated development has led to a “bag of tricks” situation that will benefit from a more systematic perspective. The workshop will address these challenges by bringing together time integration specialists with numerical PDE specialists and experts in high-performance numerical computing.

    Mathematics, Art, STEM, Research, Mathematics, Technology, Engineering
  • Humans naturally want to help each other, but modern society traps “mental health” behind expensive bureaucracies. Cheeseburger Therapy teaches ordinary people the skills of Cognitive Behavioral Therapy and provides structured oversight to make it safe for them to help other humans through the internet—and get paid doing so.

    Cheeseburger Therapy began as research at the University of Washington but is now a functioning community that changes real human lives. In this talk we will show three innovations that make it possible:

    • A novel version of Cognitive Behavioral Therapy, custom-designed for the internet, embedded in a User Interface that breaks the therapeutic process into a flowchart of steps that can be taught to ordinary humans, tracked by a computer, and evaluated as a reliable method for changing someone’s life.
    • A research platform for developing new therapeutic methods, by A/B testing them within practicing online community and evaluating results against a baseline. The platform features a novel design for text-chat that increases both empathy and anonymity, increasing signal and reducing noise within experiments.
    • A novel interactive peer-to-peer training system that can teach ordinary humans, in about 20 hours, to provide consistently helpful therapeutic conversations. Helpers improve their emotional listening, learn new thought skills, and gain the opportunity to graduate and make money helping people.

    Michael Toomim is a Computer Scientist trained at the University of Washington and the University of California at Berkeley, who currently works at the Invisible College in Berkeley. He has expertise in Human-Computer Interaction, and has worked in Cognitive Psychology, Social Computing, Data Synchronization, and Programming Tools. His PhD thesis defined the first measurable approach to Attention Economics. He currently co-leads the Cheeseburger Therapy and Braid projects.

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

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Psychology & Cognitive Sciences
  • Dec
    9

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

    This month:

    • Tracie Shea, PhD: “Treatment of Anger Problems in OEF/OIF Veterans: Results of a Randomized Controlled Trial”
    • Liz Chen, PhD: Presentation title TBA

    Registration will be available soon.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • A person with long dreadlock hair is staring upward. They have a VR headset on their head. They a...
    Dec
    9
    10:30am - 12:30pm

    VR Slowdown: Something Different for Reading Period

    Granoff Center for the Creative Arts

    Drop-in at any time between 10:30am -12:30pm

    Need a fun and inspiring way to start off reading period this semester? Why not come by and give Virtual Reality a try! We will be demoing some popular VR applications in our Occulus Quest 2 headsets. No experience necessary. Just bring yourself and your imagination!  

  •  

    Featuring
    Arun Durvasula

    Postdoctoral Research Fellow, Departments of Genetics and Human Evolutionary Biology, Harvard Medical School

     

    LINKING HUMAN EVOLUTIONARY HISTORY TO PHENOTYPIC VARIATION

     

    A central question in genetics asks how genetic variation influences phenotypic variation. The distribution of genetic variation in a population is reflective of the evolutionary forces that shape and maintain genetic diversity such as mutation, natural selection, and genetic drift. In turn, this genetic variation affects molecular phenotypes like gene expression and eventually leads to variation in complex traits. In this talk, I will discuss how both ancient and recent evolutionary events have shaped the patterns of genetic and phenotypic variation observed in populations today.

  • The Carney Institute for Brain Science is launching a new Advancing Research Careers Program for early career investigators at Brown University and its affiliated hospitals. Please join the program’s leadership team for a virtual open house on Tuesday, December 7, at 3 p.m. to learn about the program, its objectives and application process.

    Please see the attached Call for Applications for program details and application information.

    About the program

    The Carney Institute’s Advancing Research Careers (ARC) program aims to advance the research careers of women and persons historically excluded due to ethnicity and race in brain sciences at the level of advanced postdoctoral scholars and junior faculty. ARC is funded by an R25 award from NINDS and will support a cohort of up to six highly qualified participants each year through structured mentorship, research support and activities that contribute to successful neuroscience research careers. In this two-year program, selected ARC Scholars will advance a research project while expanding their mentorship network, meeting regularly with the ARC Scholar cohort, and attending professional development and quantitative skills training to advance their career goals.

    Advising, Mentorship, Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Psychology & Cognitive Sciences, Research, Training, Professional Development
  • Dr. Li-Xuan Qin, Associate Member in Biostatistics; PhD in biostatistics; Memorial Sloan Ketterin...
    Dec
    6
    12:00pm - 1:00pm

    Statistics Seminar Series | Dr. Li-Xuan Qin

    121 South Main Street

    Dr. Li-Xuan Qin, Associate Member in Biostatistics; PhD in biostatistics; Memorial Sloan Kettering Cancer

    Transcriptomics Data Normalization: Let’s Put It into Context
    This talk will describe an assessment of transcriptomics data normalization (for removing artifacts due to inconsistent experimental handling in data collection) in the context of downstream analysis. With robustly benchmarked data and novel re-sampling-based simulations, I will illustrate several caveats of data normalization for biomarker discovery, sample classification, and survival prediction. I will then discuss the underlying causes for these caveats and provide alternative approaches that are more effective for dealing with the data artifacts.

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

    Featuring
    KRYSTAL TSOSIE

    Co-Founder, Native BioData Consortium

     

    INDIGENOUS DNA AND DATA SOVEREIGNTIES IN GENOMIC AND PRECISION MEDICINE

    Indigenous people still constitute <1% of participants in precision and genomic medicine research despite endeavors to increase inclusivity. Past ethical issues related to Indigenous genomics have not been adequately reconciled and are now being repeated in the new era of Big Data. Concerns persist about the collectivization of Indigenous data into open-access databases that circumvent tribal research oversight, the underestimation of socioeconomic and cultural factors contributing to health disparities, and continued biocommercial exploitation of Indigenous biomarkers.

    Krystal will describe community-engaged research in two tribal communities and describe paths forward that center Indigenous people as the agents of access for their own genomic and health data. The future of Indigenous genomics is not mere inclusion but through recognition of Indigenous genomic and data sovereignty.

     

    Krystal Tsosie (Diné/Navajo), MPH, MA, is completing her Ph.D. in Genomics and Health Disparities at Vanderbilt University. As a geneticist-bioethicist, she co-founded the Native BioData Consortium, the first US Indigenous-led biobank, and 501c3 nonprofit institution. Her research centers on an ethical engagement with Indigenous communities in genomics and precision health. Utilizing dual quantitative and qualitative methods, she incorporates biostatistics, genetic epidemiology, public health, and computational approaches to disparities in, particularly, women’s health. Krystal’s research and educational endeavors have received international media attention in The Washington Post, NPR, New York Times, The Atlantic, Forbes, Boston Globe, among others.

  • Oren Shriki Computational Psychiatry Lab Dept. of Brain and Cognitive Sciences
    Dec
    1

    Oren Shriki, Ph.D.

    Department of Cognitive and Brain Sciences
    Ben-Gurion University of the Negev, Israel
    Abstract

    The critical brain hypothesis proposes that our brain is poised close to the border between two qualitatively different dynamical states. Whereas sub-critical dynamics are characterized by premature termination of activity propagation, super-critical dynamics are associated with runaway excitation. The talk will review evidence from recent years regarding this hypothesis and introduce the concept of neuronal avalanches, spatiotemporal cascades of activity whose sizes obey a power-law distribution. They are observed in a wide range of experiments from small-scale cortical networks to large-scale human EEG and MEG and are considered as evidence for critical brain dynamics. The avalanche analysis provides novel measures which reflect the underlying neural gain and are sensitive to changes in the balance of excitatory and inhibitory processes. Consequently, deviations from critical dynamics could serve as neuromarkers for disorders associated with altered balance. The utility of such neuromarkers will be demonstrated in several contexts, including epilepsy, prolonged wakefulness, schizophrenia, and disorders of consciousness.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research
  • Dec
    1
    12:00pm

    DSI Lecture: danah boyd

    BioMed
    PLEASE NOTE THE LOCATION CHANGE FOR THIS LECTURE. WE HAVE MOVED THE TALK TO BIOMED 291. 

     

    Featuring
    danah boyd

    Partner Researcher, Microsoft Research

    Founder and President, Data and Society

    Distinguished Visiting Professor, Georgetown University

    Visiting Professor, NYU

     

    STATISTICAL IMAGINARIES: AN ODE TO RESPONSIBLE DATA SCIENCE

    Data Science is increasingly being used to ground decision-making in both industry and public life. As data become significant and powerful, people who rely on those data come to expect certain things from all the data. All too often, data are expected to be precise, neutral, and objective. Those data are expected to speak with confidence—and not reveal their limitations. Left unchecked, data become illusory in the minds of many.

    Drawing on her research into the 2020 US Census, danah boyd will discuss how illusions surrounding data can be weaponized. She will highlight how the US Census Bureau’s decision to embrace differential privacy as part of its system to protect statistical confidentiality upended what people imagined the work of data to be. She will then turn to discuss the importance of grappling with uncertainty and limitations as a key part of responsible data science.

  • Join the Carney Institute for a conversation about early diagnosis and risk factors in Alzheimer’s disease, featuring:

    • Yu-Wen Alvin Huang, M.D., GLF Translational Assistant Professor of Molecular Biology, Cell Biology and Biochemistry
    • Hwamee Oh, Ph.D., Assistant Professor of Psychiatry and Human Behavior and Cognitive, Linguistic and Psychological Sciences

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

    Please register below to receive the Zoom link.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research
  • Dr. Jonathan Bartlett PhD, Reader in Statistics, Department of Mathematical Sciences, University of Bath, England

    Hypothetical estimands in clinical trials - a unification of causal inference and missing data methods

    In clinical trials events may take place which complicate interpretation of the treatment effect. For example, in diabetes trials, some patients may require rescue medication during follow-up if their diabetes is not well controlled. Interpretation of the intention to treat effect is then complicated if the level of rescue medication is imbalanced between treatment groups. In such cases we may be interested in a so-called hypothetical estimand which targets what effect would have been seen in the absence of rescue medication. In this talk I will discuss estimation of such hypothetical estimands. Currently such estimands are typically estimated using standard missing data techniques after exclusion of any outcomes measured after such events take place. I will define hypothetical estimands using potential outcomes, and exploit standard results for identifiability of causal effects from observational data to describe assumptions sufficient for identification of hypothetical estimands in trials. I will then discuss both ‘causal inference’ and ‘missing data’ methods (such as mixed models) for estimation, and show that in certain situations estimators from these two sets are identical. These links may help those familiar with one set of methods but not the other. They may also identify situations where currently adopted estimation approaches may be relying on unrealistic assumptions, and suggest alternative approaches for estimation.

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Nov
    18
    2:00pm

    Carney Methods Meetup: Is UMAP a lie?

    Carney Institute for Brain Science

    Note: You may also attend this event via Zoom (Meeting ID: 978 5998 6393 | Passcode: 451768). This workshop requires you to be logged into Zoom through your Brown account.

    Carney Methods Meetups are informal gatherings focused on methods for brain science, moderated by Jason Ritt, Carney’s scientific director of quantitative neuroscience. Carlos Vargas-Irwin, assistant professor (research) of neuroscience, and Tommy Hosman, research engineer at BrainGate, will join Ritt in an open discussion of current debates over the validity and interpretation of some leading methods (UMAP, t-SNE) of data dimensionality reduction. While application of dimensionality reduction to neuroscience data is important and ubiquitous, the methods are challenging to understand, with few analytic guarantees on the results. Some recent papers raise questions about whether these methods are doing what practitioners think they are doing.

    Videos and notes from previous meetups are available on the Carney Institute website.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research
  • Nov
    17
    2:00pm

    Data Science Initiative Research Seminar Series: Sydney Skybetter

    164 Angell Street, Providence, RI 02912

     

    SYDNEY SKYBETTER

    Senior Lecturer in Theatre Arts and Performance Studies, Associate Chair of Theatre Arts and Performance Studies, Brown University

     

    DO YOU LOVE ME: THE UNCANNY, DANCERLY FUTURE OF CHOREOROBOTICS

    Professor of Choreography and Emerging Technologies, Sydney Skybetter, narrates the choreographic history that bridges dancing military robots, surveillant software platforms, and centuries-old French notation practices. Through analyses of ballet history and contemporary robotics manufacturer Boston Dynamics, Skybetter sketches out the risks and opportunities of the emerging, interdisciplinary field of choreorobotics, and gives an advanced preview of a new course he will co-teach in 2022 titled Choreorobotics 0101.

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

    This month:

    • Ami Vyas, PhD: “Are We ‘Choosing Wisely’ and Improving Value in Cancer Care?”

    Registration will be available soon.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Nov
    10
    4:00pm

    Data Science Initiative Seminar: Suresh Venkatasubramanian (DSI, CS)

    85 Waterman Street, Providence RI 02909

     

    Featuring
    SURESH VENKATASUBRAMANIAN

    Computer Science and Data Science, Brown University

     

    Introduction by
    RICHARD M. LOCKE

    Provost, Brown University

     

    MACHINE READABLE: THE POWER AND LIMITS OF ALGORITHMS THAT ARE SHAPING SOCIETY

    Algorithms have infiltrated our society, imposing their own frame of reference on how we conduct ourselves, how we interact with others, and how we are judged. They’ve turbocharged inequality and biases. They’ve accelerated the balkanization of the landscape of ideas, making it easier and easier to live within suffocatingly homogeneous ideological and cultural bubbles.

  • Nov
    5
    1:00pm - 1:50pm

    BigAI Talk: David Held, CMU: “Perceptual Robot Learning”

    Watson Center for Information Technology (CIT)

    Zoom link for those choosing to join virtually: https://brown.zoom.us/j/99968764917
    Add to calendar: Google Calendar

    Abstract: Robots today are typically confined to interact with rigid, opaque objects with known object models. However, the objects in our daily lives are often non-rigid, can be transparent or reflective, and are diverse in shape and appearance. One reason for the limitations of current methods is that computer vision and robot planning are often considered separate fields. I argue that, to enhance the capabilities of robots, we should design state representations that consider both the perception and planning algorithms needed for the robotics task. I will show how we can develop novel perception and planning algorithms to assist with the tasks of manipulating cloth, manipulating novel objects, and grasping transparent and reflective objects. By thinking about the downstream task while jointly developing perception and planning algorithms, we can significantly improve our progress on difficult robots tasks.

    Bio: David Held is an assistant professor at Carnegie Mellon University in the Robotics Institute and is the director of the RPAD lab: Robots Perceiving And Doing. His research focuses on perceptual robot learning, i.e. developing new methods at the intersection of robot perception and planning for robots to learn to interact with novel, perceptually challenging, and deformable objects. David has applied these ideas to robot manipulation and autonomous driving. Prior to coming to CMU, David was a post-doctoral researcher at U.C. Berkeley, and he completed his Ph.D. in Computer Science at Stanford University. David also has a B.S. and M.S. in Mechanical Engineering at MIT. David is a recipient of the Google Faculty Research Award in 2017 and the NSF CAREER Award in 2021.

    Host: Stefanie Tellex

  •  

    APRIL WEI, Ph.D.

    Postdoctoral Researcher, UCLA (2021); Assistant Professor, Cornell University (2022)

     

    THE LINGERING EFFECTS OF NEANDERTHAL INTROGRESSION ON HUMAN COMPLEX TRAITS

    Despite a decade of investigation, the biological consequences of the archaic introgression on present-day humans are not fully understood. Compared to the rest of the genetic variants, archaic introgressed variants have lower allele frequencies and higher linkage disequilibrium scores, making statistical genetic analyses more challenging.

    To accurately assess the phenotypic impact of introgressed variants, we used 60 genome-wide UK Biobank size simulations with 20 different genetic architectures to benchmark the power and calibration of statistical genetic methods. We then apply suitable methods to analyze 235,592 Neanderthal introgressed mutations across 96 phenotypes using the white British data from the UK Biobank. Compared to modern human SNPs, none of the 96 studied phenotypes have enriched heritability in Neanderthal introgressed variants.

    In addition, about one-third of the phenotypes have a marginally depleted introgressed heritability, consistent with purifying selection on introgressed variants. Despite that, introgressed variants still contribute to phenotypic variation in studied phenotypes. Using Bayesian statistical fine-mapping, we identified 110 credible sets where introgressed variants have causal phenotypic effects (FDP = 0.156). Lastly, we report evidence for pleiotropy, additive phenotypic effect, and epistasis in the putatively causal introgressed variants.

  • Interested in being funded by LeaRRn? On November 3rd, LeaRRn will host informational webinars for those interested in becoming a LeaRRn Pilot Awardee or LHS Scholar. Register here for the Pilot Program Informational webinar, and look for the companion LHS Scholar Informational webinar.

  • Interested in being funded by LeaRRn? On November 3rd, LeaRRn will host informational webinars for those interested in becoming a LeaRRn Pilot or LHS Scholar. Register here for the LHS Scholar Informational webinar, and look for the companion Pilot Program Informational webinar.

  • Nov
    3
    11:00am - 12:30pm

    November Academic Grand Rounds

    Virtual

    Implementation Science: Driving Health Policy Change in Learning Health Systems

    Amy Kilbourne, Ph.D., MPH

    Associate Chair for Research

    Professor of Learning Health Sciences

    Director, Quality Enhancement Research Initiative (QUERI)

    U.S. Department of Veterans Affairs

    Faculty, U-M Institute for Healthcare Policy and Innovation (IHPI)

    Wednesday, November 3, 2021◊ 11:00 am - 12:30 pm

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Mathematics, Technology, Engineering, Physical & Earth Sciences, Psychology & Cognitive Sciences, Research, Social Sciences
  • Dr. Jean Feng, Assistant Professor, Department of Epidemiology and Biostatistics, University of C...
    Nov
    1

    Dr. Jean Feng, Assistant Professor, Department of Epidemiology and Biostatistics, University of California, San Francisco (UCSF)

    Safe approval policies for continual learning systems in healthcare
    The number of machine learning (ML)-based medical devices approved by the US Food and Drug Administration (FDA) has been rapidly increasing. The current regulatory policy requires these algorithms to be locked post-approval; subsequent changes must undergo additional scrutiny. Nevertheless, ML algorithms have the potential to improve over time by training over a growing body of data, better reflect real-world settings, and adapt to distributional shifts. To facilitate a move toward continual learning algorithms, the FDA is looking to streamline regulatory policies and design Algorithm Change Protocols (ACPs) that autonomously approve proposed modifications. However, the problem of designing ACPs cannot be taken lightly. We show that policies without error rate guarantees are prone to “bio-creep” and may not protect against distributional shifts. To this end, we investigate the problem of ACP design within the frameworks of online hypothesis testing and online learning and take the first steps towards developing safe ACPs.

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Need help with REDCap? Regular workshops are available through our BERD Core to all RI investigators.

    Join us on Thursday, October 28 for a new virtual workshop on REDCap’s features, as well as tips and tricks for using REDCap for your research. 

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Join Advance-CTR and the Data Science Initiative at Brown for this 5-part series exploring machine learning, its methodology, and application in biomedicine and health. The purpose of this series is to serve as an introduction to machine learning for researchers, clinician scientists, and others who may be interested in using these methods in their research.

    Wednesday, October 27 

    Joe Hogan, ScD : “The Role of Machine Learning in Predictive and Causal Inference”

    Many machine learning methods can be formulated as complex but flexible statistical models. A common and important use of the models is to generate accurate predictions from a large set of covariates. When prediction is the goal, machine learning methods can learn prediction rules that involve interactions, nonlinearities, and other characteristics of the prediction function that would be difficult to know in advance.

    In this talk I demonstrate the utility of machine learning for generating causal inferences. For large-scale observational data, causal inference typically requires the correct specification of one or more component models for the purpose of confounder adjustment. This applies to propensity score methods, inverse probability weighting, regression adjustment, and standardization. The component models are not usually of direct interest, but in cases where there are many potential confounders they can be difficult to specify in advance.

    I will first describe the key differences between predictive and causal inference. Then, using a couple of examples from HIV and infectious disease research, I will illustrate how machine learning algorithms that can be formulated as ‘proper’ statistical models play a key role in the process of generating causal inferences.

    About the Speaker

    Professor Hogan’s research concerns the development and application of statistical methods for large-scale observational missing data. He is interested in causal inference, missing data, and quantifying uncertainty associated with untestable assumptions. Nearly all of his work is motivated by applications in HIV/AIDS and infectious disease. For the past several years he has co-led an NIH-funded international training program designed to build research capacity in biostatistics at Moi University in Kenya.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Join Advance-CTR and the Data Science Initiative at Brown for this 5-part series exploring machine learning, its methodology, and application in biomedicine and health. The purpose of this series is to serve as an introduction to machine learning for researchers, clinician scientists, and others who may be interested in using these methods in their research.

    Wednesday, October 20, 2021

    Ruotao Zhang, MSc Role of Calibration in Uncertainty-based Referral for Deep Learning”

    The uncertainty in predictions from deep neural network analysis of medical imaging is challenging to assess but potentially important to include in subsequent decision making. Using data from diabetic retinopathy detection, we present an empirical evaluation of model performance and the impact of uncertainty-based referral, an approach that prioritizes referral of observations based on the magnitude of a measure of uncertainty. We consider several configurations of network architecture, method for uncertainty estimation, and training data size. We identify a strong relationship between the effectiveness of uncertainty-based referral and having a well-calibrated model. This is especially relevant as complex deep neural networks tend to have high calibration errors. Finally, we provide evidence that post-calibration of the neural network can improve uncertainty-based referral.

    Dilum Aluthge, MD, PhD student:  “Supervised Machine Learning Workflows for Electronic Health Records”

    Supervised machine learning can be used to develop clinical decision support systems for use in electronic health records (EHRs). The first portion of the talk will provide an overview of the supervised machine learning workflow. The second portion will present an example application of classification using EHR data, specifically the problem list and medication list from a patient’s chart.

    Optional Readings: 

    1. Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med. 2019 Apr 4;380(14):1347-1358. doi: 10.1056/NEJMra1814259. PMID: 30943338.

    2. Sinha I, Aluthge DP, Chen ES, Sarkar IN, Ahn SH. Machine Learning Offers Exciting Potential for Predicting Postprocedural Outcomes: A Framework for Developing Random Forest Models in IR. J Vasc Interv Radiol. 2020 Jun;31(6):1018-1024.e4. doi: 10.1016/j.jvir.2019.11.030. Epub 2020 May 4. PMID: 32376173.

    About the Speakers

    Ruotao Zhang is a PhD student in the Department of Biostatisticsunder the supervision of Dr Steingrimsson and Dr Gatsonis. Before coming to the US, he worked as a data scientist at China Resources Holdings. Ruotao graduated from University of Oxford with a MSc in Applied Statistics, and before that he obtained a BSc in Mathematics from Imperial College London. His research interests focus on statistical machine learning methods with application to biomedical data. 

    Dilum Aluthge is an MD/PhD student at the Brown Center for Biomedical Informatics, Center for Computational Molecular Biology and the Warren Alpert Medical School. His advisors are Dr. Neil Sarkar and Dr. Liz Chen. His research focuses on the theoretical concepts of learning health systems as well as the practical considerations of their implementation. Specific areas of interest include machine learning, clinical decision support, health information exchange, standards and interoperability, and physiologic reserve. Dilum earned his Bachelor of Science in Applied Mathematics at Brown. He is the co-creator of the PredictMD machine learning framework, which is implemented in the Julia programming language. He is also the founder of the JuliaHealth open source organization.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • This workshop will cover an introduction to REDCap.
    Oct
    14

    Introduction to REDCap Workshop

    Presented by: Sarah B. Andrea, PhD, MPH

    Geared toward new or novice REDCap users, this class answers “What” REDCap is, “Why” you want to use it, and goes through the entire lifecycle of a REDCap project – from initial setup to data entry and to exporting your data.

    About the Instructor

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

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • This workshop will cover an introduction to REDCap.
    Oct
    14

    Introduction to REDCap Workshop

    Presented by: Sarah B. Andrea, PhD, MPH

    Geared toward new or novice REDCap users, this class answers “What” REDCap is, “Why” you want to use it, and goes through the entire lifecycle of a REDCap project – from initial setup to data entry and to exporting your data.

    About the Instructor

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

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Join Advance-CTR and the Data Science Initiative at Brown for this 5-part series exploring machine learning, its methodology, and application in biomedicine and health. The purpose of this series is to serve as an introduction to machine learning for researchers, clinician scientists, and others who may be interested in using these methods in their research.

    Wednesday, October 13, 2021:

    Yichi Zhang, PhD: “Interpretable Individualized Treatment Rules Using Decision Lists”

    Precision medicine is currently a topic of great interest in clinical science. One typical way to formalize precision medicine is through an individualized treatment rule, which is a sequence of rules, one per each stage of intervention, that map up-to-date patient information to a recommended treatment. An optimal individualized treatment rule is defined as maximizing the mean of some cumulative clinical outcome if applied to a population of interest. In many settings, estimation of an optimal individualized treatment rule is an exploratory analysis intended to generate new hypotheses for subsequent research and not to directly dictate treatment to new patients. In such settings, a rule that is interpretable in a domain context may be of greater value than an unintelligible one built using “black-box” methods. In this talk, I will present a causal inference framework for estimating an optimal individualized treatment rule and discuss its connection to reinforcement learning. Then, I will describe an estimator of an optimal and interpretable rule, which is expressible as a list of “if-then” statements that can be presented as either a paragraph or as a simple flowchart that is immediately interpretable to domain experts. The proposed method will be illustrated using a clinical trial dataset.

    About the Speaker

    Yichi Zhang is an assistant professor in the Department of Computer Science and Statistics at the University of Rhode Island. He received his PhD in Statistics from North Carolina State University and received postdoctoral training at Harvard School of Public Health. His research focuses on data-driven decision-making and sequential causal inference with applications to biomedical problems.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Join Advance-CTR and the Data Science Initiative at Brown for this 5-part series exploring machine learning, its methodology, and application in biomedicine and health. The purpose of this series is to serve as an introduction to machine learning for researchers, clinician scientists, and others who may be interested in using these methods in their research.

    Friday, October 8, 2021: 

    Roberta De Vito, PhD : “Cross-Study Machine Learning Techniques: Reproducibility and Differences Across Studies”

    Biostatistics and computational biology are increasingly facing the urgent challenge of efficiently dealing with a large amount of experimental data. In particular, high-throughput assays are transforming the study of biology, as they generate a rich, complex, and diverse collection of high-dimensional data sets. Through compelling statistical analysis, these large data sets lead to discoveries, advances and knowledge that were never accessible before, via compelling statistical analysis. Building such systematic knowledge is a cumulative process which requires analyses that integrate multiple sources, studies, and technologies. The increased availability of ensembles of studies on related clinical populations, technologies, and genomic features poses four categories of important multi-study statistical questions: 1) To what extent is biological signal reproducibly shared across different studies? 2) How can this global signal be extracted? 3) How can we detect and quantify local signals that may be masked by strong global signals? 4) How do these global and local signals manifest differently in different data types? We will answer these four questions by introducing a novel class of methodologies for the joint analysis of different studies. The goal is to separately identify and estimate 1) common factors reproduced across multiple studies, and 2) study-specific factors. We present different medical and biological applications. In all the cases, we clarify the benefits of a joint analysis compared to the standard methods. Our method could accelerate the pace at which we can combine unsupervised analysis across different studies, and understand the cross-study reproducibility of signal in multivariate data.

    About the Speaker

    Roberta De Vito is a statistician with a passion for teaching and developing statistical tools for cancer research and disorder risk, with particular focus on epidemiology and genomics. Currently, she is Assistant Professor in the department of Biostatistics and at the Data Science Initiative at Brown University. She completed her Ph.D. in Statistical Science at the University of Padua, advised by Giovanni Parmigiani at Harvard University where she developed her thesis work. The main research interest is latent variable model, Bayesian non parametric, variable selection via sparsity prior, machine learning and big data with particular focus on genomics and epidemiology. She was a postdoc at Princeton University in Barbara Engelhardt’s group where she developed Bayesian and latent variable discrete model in high-dimensional biological and epidemiological data. Her passion for teaching developed at Princeton University where she taught some classes and had the opportunity to mentor Master and PhD students. Some of her previous mentees are now pursuing successful research careers in biostatistics and data science also across Ivy League universities, like Harvard University, Princeton and MIT. Her website https://rdevito.github.io/web/ provides complete details.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Oct
    6
    1:00pm - 1:50pm

    BigAI Talk: Siddharth Srivastava “Assistive AI and the Unusual Effectiveness of Abstractions”

    Watson Center for Information Technology (CIT)

    **This talk will be held both in-person in CIT 368, and through a virtual option: https://brown.zoom.us/j/93695915402 Meeting ID: 936 9591 5402**

    Abstract: Can we balance efficiency and reliability while designing assistive AI systems? What would such AI systems need to provide? In this talk I will present some of our recent work addressing these questions. In particular, I will show that a few fundamental principles of abstraction are surprisingly effective in designing efficient and reliable AI systems that can plan and act over multiple timesteps. Our results show that abstraction mechanisms are invaluable not only in improving the efficiency of sequential decision making, but also in developing AI systems that can explain their own behavior to non-experts, and in computing user-interpretable assessments of the limits and capabilities of Black-Box AI systems. I will also present some of our work on learning the requisite abstractions in a bottom-up fashion. Throughout the talk I will highlight the theoretical guarantees that our methods provide along with results from empirical evaluations featuring decision-support/digital AI systems and physical robots.

    Bio: Siddharth Srivastava is an Assistant Professor of Computer Science in the School of Computing and Augmented Intelligence at Arizona State University. Prof. Srivastava was a Staff Scientist at the United Technologies Research Center in Berkeley. Prior to that, he was a postdoctoral researcher in the RUGS group at the University of California Berkeley. He received his PhD in Computer Science from the University of Massachusetts Amherst. His research interests include robotics and assistive AI, with a focus on reasoning, planning, and acting under uncertainty. His work on integrated task and motion planning for household robotics has received coverage from international news media. He is a recipient of the NSF CAREER award, a Best Paper Award at the International Conference on Automated Planning and Scheduling (ICAPS) and an Outstanding Dissertation Award from the Department of Computer Science at UMass Amherst. He served as conference chair for ICAPS 2019 and currently serves as an Associate Editor for the Journal of AI Research.

    (Please note the speaker will deliver this talk virtually in CIT 368.)

    Host: George Konidaris

  • Oct 6 CHRHS Event
    Oct
    6
    12:00pm - 1:00pm

    Data for Good: Improving Humanitarian Mine Action Through Open Source Conflict Data

    Watson Institute for International and Public Affairs

    Since 2011, the use of explosive weapons has proliferated in Syria during the conflict. Due to a variety of factors, a percentage of these fail to detonate or are abandoned during the fighting. These explosive remnants of war (ERW), in addition to landmines and improvised explosive devices (IEDs), pose an enduring, multi-generational threat to a population long after the violence has stopped. While on the ground operations by humanitarian mine action (HMA) actors are among the most effective ways to deal with the physical threat of ERW, landmine, and IED contamination, this is not always possible due to various restrictions and instead, HMA actors turn to desk-based assessments involving data.

    In this presentation, Jonathan Robinson will discuss his experience working with the Carter Center to lead the development of an innovative methodology using open-source conflict data to infer where potential areas of explosive weapons contamination may be located. He will also present initial findings from this work that are featured in the latest edition of the Journal for Conventional Weapons Destruction.

    Jonathan Robinson specializes in aid worker security and conflict analysis. He has previously focused on Syria through his work as a senior researcher for the Carter Center’s Syria Conflict Resolution Program and as a safety advisor for the International NGO Safety Organization’s (INSO) Syria team. Prior to joining the humanitarian sector, Jonathan worked as a security analyst for a British private security company in Qatar, the United Arab Emirates, and southern Iraq. He has also supported the United Nations Department of Safety and Security (UNDSS) in Syria, the Halo Trust’s Yemen Program, and Caritas Switzerland’s Jordan, Lebanon, and Syria Programs with advice and research. Jonathan holds a MSc in Islamic and Middle Eastern Studies from the University of Edinburgh and BA (honors) in Archaeology from Durham University.

  • Join Advance-CTR and the Data Science Initiative at Brown for this 5-part series exploring machine learning, its methodology, and application in biomedicine and health. The purpose of this series is to serve as an introduction to machine learning for researchers, clinician scientists, and others who may be interested in using these methods in their research.

    Friday, October 1, 2021:

    Carsten Eickhoff, PhD: “An Introduction to Machine Learning” 

    This talk will introduce the basic components of machine learning systems and research papers and introduce the notion of casting inference problems in terms of features and labels. We will discuss supervised classification and regression techniques as well as altogether unsupervised learning schemes. We will have a look at reinforcement learning, model optimization and evaluation and close with a discussion of model generality and the curse of dimensionality.

    About the Speaker

    Dr. Eickhoff is an Assistant Professor of Medical and Computer Science at Brown University where he leads the Biomedical AI Lab, specializing in the development of data science and information retrieval techniques with the goal of improving patient safety, individual health and quality of medical care. Prior to joining Brown, he graduated from The University of Edinburgh and TU Delft, and was a postdoctoral fellow at ETH Zurich and Harvard University. Carsten has published more than 100 articles in computer science conferences (SIGIR, EMNLP, NAACL, WWW, KDD, WSDM, CIKM) and clinical journals (Nature Digital Medicine, The Lancet - Respiratory Medicine, Radiology, European Heart Journal). His research has been supported by the NSF, NIH, DARPA, IARPA, Google, Amazon, Microsoft and others. Aside from his academic endeavors, he is a founder and board member of several deep technology startups in the health sector that strive to translate technological innovation to improved safety and quality of life for patients. 

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Lisa Juckett presents, Identifying Contextual Determinants & Implementation Strategies: Stakeholder-Engaged Initiatives to Improve Rehabilitation Research and Practice.

    This presentation will review two national-level initiatives, both guided by the Consolidated Framework for Implementation Research, that aim to advance the use of evidence in rehabilitation practice.

    This event is part of the LeaRRn series, “Using Health System Research to Revolutionize Rehabilitation Care.”

  • Sep
    24

    Speaker:  Marissa Kawehi Loving, Assistant Professor, Georgia Institute of Technology

    Title: Where do I belong? Creating space in the math community.

    Abstract: I will tell (an abridged) story of my mathematical journey. I will be blunt about many of the ups and downs I have experienced and touch on some of the barriers I have encountered. I will also discuss some of the programs and spaces I have helped create in my quest to make the mathematics community into a place where folks from historically underrepresented groups (particularly women of color) can feel safe (from harassment and abuse), seen (as valued members of the mathematical community), and free to devote their energy to their work. If you have ever felt like you don’t belong or worried that you have made others feel that way, this talk is for you.

    Bio: Marissa Kawehi Loving is an NSF Postdoctoral Research Fellow and Visiting Assistant Professor in the School of Math at Georgia Tech. She graduated with her PhD in mathematics in August 2019 from the University of Illinois at Urbana-Champaign where she was supported by an NSF Graduate Research Fellowship and an Illinois Graduate College Distinguished Fellowship. Marissa was born and raised in Hawai’i where she completed her B.S. in Computer Science and B.A. in Mathematics at the University of Hawai’i at Hilo. She is the first Native Hawaiian woman to earn a PhD in mathematics. She is also Black, Puerto Rican, and Japanese. Her research interests are in geometry/topology, especially mapping class groups of surfaces (of both finite and infinite type). Marissa is also deeply invested in making the mathematics community a more equitable place. Some of her work includes mentoring undergraduate research (through programs such as [email protected], MSRI-UP, and the Georgia Tech School of Math’s REU) and co-organizing initiatives like SUBgroups and paraDIGMS.

  • What can academic scientists learn from the trading world to become more comfortable with taking measured risks in research?

    Join the Carney Institute for Brain Science for a lively discussion about finding the right balance between risk-taking, failure and success, featuring two members of the President’s Advisory Council for the Carney Institute who are leading innovators in the trading and investing world.

    • Nancy Zimmerman, a 1985 Brown graduate, a trustee of the University’s Corporation and the co-founder and managing partner of Bracebridge Capital, a leading Boston-based hedge fund manager with over $12 billion under management
    • Adam Korn, a 1997 Brown graduate and chief information officer at the $47 billion investment firm Sixth Street Partners

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

    Get to Know the Speakers

    Nancy Zimmerman is co-founder and managing partner of Bracebridge Capital, a leading Boston-based hedge fund manager with over $12 billion under management. Bracebridge is a pioneer in the field of absolute return investing and for over 25 years has focused on generating returns that are largely uncorrelated with broad moves in equities, currencies and rates. Bracebridge manages private investment funds serving longstanding investors that include endowments, foundations, family offices and pensions. Zimmerman began her career at O’Connor & Associates and managed the interest rate option group on a worldwide basis for Goldman Sachs before founding Bracebridge. She earned an A.B. in Economics and the Practice and Production of Art from Brown University in 1985. Zimmerman is a Trustee of the Corporation of Brown University and the inaugural chair of the President’s Advisory Council for Brown’s Carney Institute for Brain Science. At other institutions — including the Transformative Scholars Program in Neurology at Massachusetts General Hospital and the Ragon Institute of MGH, MIT and Harvard — Zimmerman has led a series of initiatives that fund early-career investigators. During 2020, she helped fund and promote cutting-edge research on COVID-19. She is a member of the Board of Directors of Social Finance U.S., a nonprofit that tackles complex social challenges through innovative public private partnerships.

    Adam Korn is chief information officer at the $47 billion investment firm Sixth Street Partners. Previously, Korn was the global head of Securities Division Engineering at Goldman Sachs, focusing on Systematic Market Making and Marquee, the firm’s client-facing digital platform for institutional clients. He served as a member of the Securities Division Automated Trading Controls Committee, Partnership Committee and Firmwide Technology Risk Committee. Korn joined Goldman Sachs in 2002 as an associate in the Equity Derivatives Research Group, focusing on market microstructure research. In 2007, he led the team that won the Michael P. Mortara Award for Innovation. Korn was named managing director in 2008 and partner in 2010. Prior to joining the firm, Korn was the co-founder and chief financial officer of a software startup. He began his career in 1997 at Credit Suisse First Boston in Investment Banking. Adam serves as vice chairman of the Board of Trustees of the Bobby Jones Chiari & Syringomyelia Foundation and as a member of Brown’s Carney Institute for Brain Science Advisory Council.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research
  •    Dr. Amanda Meija, Assistant Professor, Department of Statistics, Indiana University
    Sep
    20

    Dr. Amanda Meija, Assistant Professor, Department of Statistics, Indiana University

    Using empirical population priors to provide accurate subject-level insights into functional brain organization through template ICA

    Abstract: A primary objective in resting-state fMRI studies is localization of functional areas (i.e. resting-state networks) and the functional connectivity (FC) between them. These spatial and temporal properties of brain organization may be related to disease progression, development, and aging, making them of high scientific and clinical interest. Independent component analysis (ICA) is a popular tool to estimate functional areas and their FC. However, due to typically low signal-to-noise ratio and short scan duration of fMRI data, subject-level ICA results tend to be highly noisy and unreliable. Thus, group-level functional areas are often used in lieu of subject-specific ones, ignoring inter-subject variability in functional topology. These group-average maps also form the basis for estimating FC, leading to potential bias in FC estimates given the topological differences in underlying functional areas. An alternative to these two extremes (noisy subject-level ICA and one-size-fits-all group ICA) is Bayesian hierarchical ICA, wherein information shared across subjects is leveraged to improve subject-level estimation of spatial maps and FC. However, fitting traditional hierarchical ICA models across many subjects is computationally intensive. Template ICA is a computationally convenient hierarchical ICA framework using empirical population priors derived from large fMRI databases or holdout data. Template ICA produces more accurate and reliable estimates of subject-level functional areas compared with popular ad-hoc approaches. The flexible Bayesian framework also facilitates incorporating other sources of a-priori information. In this talk, I will describe the template ICA framework, as well as two extensions to the baseline model: the first incorporates spatial priors to leverage information shared across neighboring brain locations, and the second incorporates empirical population priors on the FC between functional areas. I will also present recent findings from a study of the effects of psilocybin (the prodrug compound found in “magic mushrooms”) on the organization of the thalamus.
    Bio: Mandy Mejia is an assistant professor in the Department of Statistics at Indiana University. Her research aims to develop statistical techniques to extract accurate individual insights from functional MRI data, which is noisy, big and complex. Her group pursues this goal in three primary ways: (1) developing computationally efficient Bayesian techniques, which leverage information shared across space and across individuals to produce more accurate estimates at the individual level; (2) developing statistically principled noise-reduction techniques, and (3) analyzing data on the cortical surface and subcortical gray matter to facilitate spatial modeling and improve inter-subject alignment. Her group has developed several software tools to facilitate cortical surface and Bayesian analysis of fMRI data in R.
    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Call for Applications! Apply for the Advance-CTR Pilot Projects Program. We’re funding five to eight projects for one-year research awards in two categories:

    1. Proposals with a single PI may apply for $37,500 in direct costs.
    2. Proposals involving multi-PIs from different disciplines may apply for up to $75,000 in direct costs.

    About the Pilots

    The Pilot Projects Program brings investigators together from institutions across the state to develop interdisciplinary collaborations that span the translational research spectrum. The program funds a variety of research that addresses Rhode Island’s health challenges and community health priorities.

    Key Dates & Deadlines

    • August 31, 2021: Last day to schedule calls with leadership

    • September 14, 2021: Preliminary applications due

    • November 8, 2021: Invited, full proposals due

    The anticipated performance period is February 1, 2022 to January 30, 2023.

    Application Resources

    Don’t go at it alone. Schedule a call with our program leadership to discuss your questions, read up on the eight elements of a successful preliminary application, get tips for preparing your application, and review two examples from investigators who have successfully applied to the program.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Dr. Alyssa Bilinski, Brown University, Department of Health Services, Policy & Practice
    Sep
    13
    12:00pm - 12:50pm

    Statistics Seminar Series | Dr. Alyssa Bilinski

    121 South Main Street

    Dr. Alyssa Bilinski, Brown University, Department of Health Services, Policy & Practice

    O Decision Tree, O Decision Tree: Interpretable classification metamodels for health policy (w/Nicolas Menzies, Jeffrey Eaton, John Giardina, and Joshua Salomon)

    Over the past decade, researchers have developed a rich set of metamodeling techniques for complex decision analytic models. These create parsimonious model emulators, improving the tractability of computationally-intensive analyses. However, such techniques typically focus on reproducing a full model, requiring high fidelity to the full space of parameters and outcomes, and can be difficult to interpret. In this paper, we use decision tree classifiers to create metamodels of policy-important binary outcomes. We first detail methods to fit and test classifiers optimizing out-of-sample performance, to upsample strategically in regions of high uncertainty, and to develop and test interpretable decision rules for policymakers. We apply these to a previously published agent-based simulation model of COVID-19 transmission in schools, with >99% out-of-sample predictive validity and minimal training data requirements. We compare the identified decision rules to those proposed by policymakers and to output from alternative metamodels. Our approach can reduce the computational and analytic burden of creating a metamodel, optimize performance for decisions of interest and comparability across models, and provide interpretable, easy-to-update summaries for policymakers

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

    This month: 

    Registration will be available soon.

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • This workshop will cover an introduction to REDCap.
    Sep
    9

    Introduction to REDCap Workshop

    Presented by: Sarah B. Andrea, PhD, MPH

    Geared toward new or novice REDCap users, this class answers “What” REDCap is, “Why” you want to use it, and goes through the entire lifecycle of a REDCap project – from initial setup to data entry and to exporting your data.

    About the Instructor

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

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research, Training, Professional Development
  • This workshop will cover an introduction to REDCap.
    Sep
    9

    Introduction to REDCap Workshop

    Presented by: Sarah B. Andrea, PhD, MPH

    Geared toward new or novice REDCap users, this class answers “What” REDCap is, “Why” you want to use it, and goes through the entire lifecycle of a REDCap project – from initial setup to data entry and to exporting your data.

    About the Instructor

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

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research, Training, Professional Development
  • A challenge for mathematical modeling, from toy dynamical system models to full weather and climate models, is applying data assimilation and dynamical systems techniques to models that exhibit chaos and stochastic variability in the presence of coupled slow and fast modes of variability. Recent collaborations between universities and government agencies in India and the United States have resulted in detailed observations of oceanic and atmospheric processes in the Bay of Bengal, the Arabian Sea, and the Indian Ocean, collectively observing many coupled modes of variability. One key target identified by these groups was the improvement of forecasts of variability of the summer monsoon, which significantly affects agriculture and water management practices throughout South Asia. The Monsoon Intraseasonal Oscillation is a northward propagating mode of precipitation variability and is one of the most conspicuous examples of coupled atmosphere-ocean processes during the summer monsoon. Simulating coupled atmosphere-ocean processes present mathematical challenges spanning numerical methods, data assimilation, stochastic modeling, dynamical systems and chaos, and uncertainty quantification. Predicting monsoon variability is one of the hardest, most important forecasting problems on earth due to its impact on billions of people, a key aspect of the desire to push weather forecasts into the management-actionable “medium-range” horizon of weeks to seasons. Addressing this challenge requires an interdisciplinary effort to combine observations, computation, and theory. A better understanding of these processes and how they can be represented in a variety of coupled ocean-atmosphere simulations and models (including statistical and dynamical approaches) and forecast systems (including data assimilation techniques and uncertainty quantification) is the primary topic of this workshop. While the set of observations to be discussed will emphasize this region, the mathematical and computational aspects of the program will be significantly broader, covering: coupled ocean-atmosphere modeling for weather models, climate models and idealized models; theory of the atmospheric and oceanic boundary layers, and waves on the interface; data assimilation in coupled modeling systems; and numerical methods for coupled systems.

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

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

    Biology, Medicine, Public Health, Research, Training, Professional Development
  • Join us for an informational webinar about the 2021 Advance-K Scholar Career Development Program.

    Program leadership will discuss what the program has to offer to prospective participants and how to submit a competitive application. Previous Scholars will share their experience with the program, and webinar attendees may ask questions about the program and application process. 

    About the Program

    Advance-K is an intensive, 1-year program that trains investigators on how to prepare and submit a competitive NIH K award or equivalent by the end of the program. All investigators at Brown, URI, and the affiliated hospitals are eligible to apply (Deadline: September 3, 2021).

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Research, Training, Professional Development
  • What does the next generation of scientists think of the future of brain science research? 

    Brown University graduate students and a recent alum will join the Carney Institute on July 27 for an engaging conversation about their research experiences and where they think the field is headed. This event will feature:

    • Kaitlyn Hajdarovic, Ph.D. candidate in the Neuroscience Graduate Program
    • Marc Powell, postdoctoral associate at the University of Pittsburgh Department of Neurological Surgery. Powell received a Ph.D. in biomedical engineering from Brown in January 2021.
    • Jae-Young Son, Ph.D. student in cognitive, linguistic and psychological sciences

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

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Psychology & Cognitive Sciences, Research
  • This workshop will cover the ins & outs of using REDCap for electronically consenting research participants and provide a guided demonstration of the e-Consent template. Participants will learn how to design and customize e-Consent surveys and learn best practices and common pitfalls to avoid.

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

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

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

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

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

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

    June

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

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

    Brown authentication is required.

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

     

    ROBERTA DE VITO

    Assistant Professor of Data Science and Biostatistics

     

    MULTI-STUDY FACTOR ANALYSIS IN GENOMIC AND EPIDEMIOLOGICAL DATA

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

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

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

     

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

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

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

     

    BRENDA RUBENSTEIN

    Joukowsky Family Assistant Professor of Chemistry

     

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

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

  • What is CRISPR? How does gene editing work? 

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

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

    Watch previous conversations on the Carney Institute website.

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

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

     

    PIETER KLEER

    Assistant Professor, Tilburg University

     

    SECRETARY AND ONLINE MATCHING PROBLEMS WITH MACHINE LEARNED ADVICE

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

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

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

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

     

    BIOGRAPHY

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

     

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

     

    Data Wednesdays

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

  •  

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

     

    TINA ELIASSI-RAD

    Professor of Computer Science, Northeastern University

    GEOMETRIC AND TOPOLOGICAL GRAPH ANALYSIS FOR MACHINE LEARNING APPLICATIONS

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

    BIOGRAPHY

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

     

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

     

    Data Wednesdays

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

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

    April

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

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

    MATTHEW HAHN

    Distinguished Professor, Departments of Biology and Computer Science,

    Director, Center for Genomics and Bioinformatics, University of Indiana

     

    THE EVOLUTION OF MAMMALIAN MUTATION RATES

     

    BIOGRAPHY

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

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

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

     

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

     

    Data Wednesdays

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

  •  

     

    DEEPS COLLOQUIUM

     

    Jnaneshwar Das

    Assistant Research Professor, Arizona State University

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

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

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

     

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

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

    MADZA FARIAS-VIRGENS

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

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

     

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

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

     

    BIOGRAPHY

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

     

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

     

    Data Wednesdays

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

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

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

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

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

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

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

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

    Speaker Bios

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

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

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

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

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

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

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

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

  • Hyunseung Kang, PhD

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

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

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

     

    SEAN MONAGHAN, MD, FACS

    Assistant Professor of Surgery, Brown University

     

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

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

     

    BIOGRAPHY

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

     

    (F4F) Faculty for Faculty Research Talks

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

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

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

    ALICE PAUL

    Assistant Professor of Biostatistics, Brown University

    CLUSTERING WITH ITERATED LINEAR OPTIMIZATION

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

     

    BIOGRAPHY

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

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

     

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

     

    Data Wednesdays

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

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

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

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

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

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

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

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

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

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

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

    Carnegie Mellon University

    Please Note: Brown Login Required For This Talk

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

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

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

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

    Host: Ritambhara Singh

  •  

    Ph.D. Thesis Defense
    DHANANJAY BHASKAR

    Ph.D. Candidate, BME, Brown University

    “Topological Data Analysis of Collective Motion”

     

    Wednesday, March 17

    10:00 AM EDT

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

    Title: Bayesian Finite Population Survey Sampling from Spatial Process Settings

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

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

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

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

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

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

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

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

    Facebook AI Research

    Please Note: Brown Login Required For This Talk

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

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

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

    Host: George Konidaris

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

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

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

    Featured speakers and projects include, among others:

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

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

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

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

    March

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

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

    SUMAN BERA

    Postdoctoral Researcher, UC Santa Cruz

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

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

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

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

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

     

    BIOGRAPHY

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

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

     

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

     

    Data Wednesdays

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

  • Dr. Feng Liang

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

    Title: Learning Topic Models: Identifiability and Rate of Convergence

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

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

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

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

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

    Riki Conrey

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

    Michael Slaby ’02

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

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

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


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

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

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

    CHARALAMPOS “BABIS” TSOURAKAKIS

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

    ALGORITHMIC ADVANCES IN DENSE SUBGRAPH DISCOVERY

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

     

    BIOGRAPHY

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

     

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

     

    Data Wednesdays

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

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

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

    This week: 

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

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

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

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

    Advance-CTR Introduction to REDCap

    Zoom

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

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

    Noah Harding Professor of Statistics, Rice University

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

    Abstract:

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

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

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

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

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

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

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

    ROBERT GHRIST

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

     

    OPINION DYNAMICS ON SHEAVES

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

     

    BIOGRAPHY

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

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

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

     

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

     

    Data Wednesdays

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

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

    Wednesday, February 24, 2021

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

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

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

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

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

    PONTUS SKUGLAND

    Group Leader, Ancient Genomics Laboratory, Crick Institute

    TRACKING THE GENOMIC HISTORY OF DOGS AND HUMANS

    (Zoom Link)

     

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

    ROBERT GHRIST

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

    OPINION DYNAMICS ON SHEAVES

    (Zoom Link)

     

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

     

    Data Wednesdays

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

  •  

    PONTUS SKUGLAND

    Group Leader, Ancient Genomics Laboratory, Crick Institute

    TRACKING THE GENOMIC HISTORY OF DOGS AND HUMANS

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

     

    BIOGRAPHY

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

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

     

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

     

    Data Wednesdays

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

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



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

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

     

    RITAMBHARA SINGH

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

     

    TOWARDS DATA INTEGRATION IN GENOMICS USING MACHINE LEARNING

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

    BIOGRAPHY

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

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

     

    Fair February

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

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

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

     

    Featuring Dr. Fizza Gillani, PhD, CPEHR

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

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

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

     

     

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

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

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

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

    BIOGRAPHY

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

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

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

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

     

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

     

    Data Wednesdays

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

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

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

    Wednesday, February 17, 2021

    The Power of Designing with Patients

    Aaron J. Horowitz
    Co-Founder & CEO
    Sproutel

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

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

    Stanford University

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

     

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

     

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

     

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

     

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

     

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

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

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

    WEEK THREE: COMPUTATION AND DEMOCRACY

     

    SESSION ONE SPEAKERS

     

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

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

    This talk is scheduled for 11:35am.

     

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

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

    This talk is scheduled for 12:10pm.

     

    SESSION TWO SPEAKERS

     

    FAIR ALGORITHMS FOR CLUSTERING

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

    This talk is scheduled for 2:05pm.

     

    AUDITING WIKIPEDIA’S HYPERLINKS NETWORK ON POLARIZING TOPICS

    Cristina Menghini,  Ph.D. Candidate, Sapienza University

    This talk is scheduled for 2:30pm.

     

    REDUCING STRUCTURAL BIAS IN NETWORKS BY LINK INSERTION

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

    This talk is scheduled for 3:00pm.

     

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

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

    See the full schedule here.

     

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

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

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

    Notes from previous Meetups are available online.

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

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

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

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

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

    February

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

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

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

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

    WEEK THREE: COMPUTATION AND DEMOCRACY

     

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

     

    DESIGNING FOR DEMOCRACY: HOW TO BUILD COMMUNITY IN DIGITAL ENVIRONMENTS

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

    BIOGRAPHY

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

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

     

    HATE SPEECH ON SOCIAL MEDIA IN INDIA

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

    BIOGRAPHY

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

     

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

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

    See the full schedule here.

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

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

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

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

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

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

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

    WEEK TWO: COMPUTATION AND WELFARE/POLICY-MAKING

     

    SESSION ONE SPEAKERS

     

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

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

    This talk is scheduled for 11:30am.

     

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

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

    This talk is scheduled for 12:10pm.

     

    SESSION TWO SPEAKERS

     

    (MACHINE) LEARNING WHAT POLICYMAKERS VALUE

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

    This talk is scheduled for 2:05pm. 

     

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

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

    This talk is scheduled for 2:30pm. 

     

    FAIR COMPARISONS WITH OPTIMAL TRANSPORT

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

    This talk is scheduled for 3:00pm.

     

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

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

    See the full schedule here.

     

  • Fair February: Data Science for Social Good

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

    WEEK TWO: COMPUTATION AND WELFARE/POLICY-MAKING

     

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

    MANIPULATION-PROOF MACHINE LEARNING

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

    BIOGRAPHY

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

     

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

    FAIR MACHINE LEARNING UNDER PARTIAL COMPLIANCE

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

    BIOGRAPHY

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

     

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

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

    See the full schedule here.

  • Feb
    3
    3:00pm - 4:30pm

    Advance-CTR Introduction to REDCap

    Zoom

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

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

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

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

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

    WEEK ONE: COMPUTATION AND HEALTH

     

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

    Erisa Terolli, Postdoctoral Research Associate, Max Plank Institute

    This talk is scheduled for 11:30am.

     

     
    TOPOLOGICAL AND GEOMETRIC METHODS FOR COVID-19 TRACKING

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

    This talk is scheduled for 12:10pm. 

     

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

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

    See the full schedule here.

     

  • Fair February: Data Science for Social Good

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

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

     

    Artificial Intelligence in Healthcare

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

    Biography

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

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

     

    Identifying Subgroups with Differential Prediction Accuracy

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

    Biography

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

     

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

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

  • Fair February 2021
    Jan
    27

     

    FAIR FEBRUARY:

    DATA SCIENCE FOR SOCIAL GOOD 

    January 27 – February 12, 2021

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

    Week 1: Computation and Health

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

    FridayYoung Researchers’ Talks

    Week 2: Computation and Social Welfare

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

    FridayYoung Researchers’ Talks

    Week 3: Computation and Democracy

    Wednesday -

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

    FridayYoung Researchers’ Talks

    Complete schedule here


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

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

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

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

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

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

    Headshot of Dr. Kim

     

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

     

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

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

    Register here to attend.

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

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

     

    GENETIC HITCHHIKING IN POPULATIONS WITH VARIABLE SIZE

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

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

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

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

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

     

    BIOGRAPHY

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

     

    Data Wednesday Seminar Series

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

     

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

    View recent Data Wednesday talks here.

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

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

    For more information and courses, please visit the University Bulletin

     

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

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

    January

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

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

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

    Advance-CTR Introduction to REDCap

    Zoom

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

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

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

     

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

     

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

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

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

     

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

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

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

     
    BIOGRAPHY

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

     

    Data Wednesday Seminar Series

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

     

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

     

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

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

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

    The Decoding Disparities Lecture Series

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

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

    Continuing Medical Education Credit

    This live activity is approved for AMA PRA Category 1 Credit

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

    decodingdisparities
  •  
    MARTA GONZALEZ

    Associate Professor of City and Regional Planning, UC Berkeley

     

    CASES OF STUDY IN COMPUTATIONAL URBAN SCIENCE

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

     
    BIOGRAPHY

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

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

     

    Data Wednesday Seminar Series

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

     

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

     

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

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

    Register now!

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

    Brown University Center for Computational Molecular Biology

     

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

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

     
    BIOGRAPHY

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

     

     

    Data Wednesday Seminar Series

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

     

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

  • Dec
    9
    12:00pm - 1:30pm

    Introduction to REDCap

    Zoom

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

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

    Register now!

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

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

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

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

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

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

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

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

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

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

    Assistant Professor of Psychology and Data Science, NYU

    LEARNING THROUGH THE EYES OF A CHILD

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

     
    Biography

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

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

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

     

    Follow Brenden on Twitter: @LakeBrenden

     

    DSI & CCMB Data Wednesday Seminar Series

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

  • XIHONG LIN

    Professor and Former Chair, Department of Biostatistics

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

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

    Associate Member of the Broad Institute of Harvard and MIT

     

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

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

     
    Biography:

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

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

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

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

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

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

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

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

    Research Mathematical Statistician

    Mathematical Statistics Research Center

    U. S. Bureau of Labor Statistics

    Title:  Pseudo Posterior Mechanism under Differential Privacy

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

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


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

    For more information about the Statistics Seminar Series go here.

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

     

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

     

    BRENDA RUBENSTEIN

    Joukowsky Family Assistant Professor of Chemistry, Brown University

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

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

     
    Biography:

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

     

    Faculty for Faculty Research Talks

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

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

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

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

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

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

    Notes from previous Meetups are available online.

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

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

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

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

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

     

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

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

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

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

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

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

    Abstract:

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

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

    Assistant Professor, Ecology and Evolutionary Biology, UCLA

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

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

    DSI & CCMB Data Wednesday Seminar Series

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

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

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

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

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

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

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

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

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

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

  • Juned Siddique, DrPH

    Associate Professor

    Departments of Preventive Medicine and Psychiatry and Behavioral Sciences

    Northwestern University Feinberg School of Medicine

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

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

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

    For more information about the Statistics Seminar Series go here.

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

     

    ELI UPFAL

    Professor of Computer Science, Brown University

     

    BIG DATA: Where Practice Meets Theory

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

     

    (F4F) Faculty for Faculty Research Talks

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

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

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

    Register now!

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

    DAMON CENTOLA

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

    Director of the Network Dynamics Group

    Senior Fellow, Leonard Davis Institute of Health Economics

    University of Pennsylvania

     

    HOW SIMILAR MEANING SYSTEMS EMERGE ACROSS DIVERSE CULTURES

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

    BIOGRAPHY

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

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

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

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

     

    Data Wednesdays

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

  • Nov
    11
    12:00pm - 1:30pm

    Introduction to REDCap

    Zoom

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

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

    Register now!

    Biology, Medicine, Public Health, Research, Training, Professional Development