• Kristin Linn, PhD
    Assistant Professor
    Department of Biostatistics
    University of Pennsylvania

    “Confounding in Imaging-based Predictive Modeling”

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

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

    Evolutionary Dynamics of Gene Regulation

    Watson Center for Information Technology (CIT)

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

    Evolutionary Dynamics of Gene Regulation

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

    Hosted by Sorin Istrail

  • Oct
    2:00pm - 4:00pm

    Healthcare Needs Innovation. Yours!

    116 Chestnut St Providence, RI 02903

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

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

    Workshop Agenda:

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

    Coffee and refreshments provided.

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


    Advising, Mentorship, Biology, Medicine, Public Health, Careers, Recruiting, Internships, Entrepreneurship, Mathematics, Technology, Engineering, Research, Training, Professional Development
  • Oct
    4:00pm - 5:00pm

    DSI Colloquia: Thomas Serre

    Watson Center for Information Technology (CIT)

    More information to be posted.

    Education, Teaching, Instruction, Graduate School, Postgraduate Education, Mathematics, Technology, Engineering
  • José R. Zubizaretta, PHD
    Associate Professor, Department of Health Care Policy, Harvard Medical School
    Faculty Affiliate, Department of Statistics , Faculty of Arts and Sciences
    Harvard University

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

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

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


DSI Colloquia take place in CIT 241 (Swig Boardroom), at 4 pm unless otherwise noted. 

september 21

Michael Littman

Professor of Computer Science, Brown University
Joseph Cappelleri
Executive Director of Biostatistics, Pfizer Inc.
Adjunct Professor of Biostatistics, Brown University
Thomas Serre
Associate Professor of Cognitive, Linguistic, and Psychological Sciences; Brown University