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Refining the Concept of Scientific Inference When Working With Big Data: A Workshop

June 8-9, 2016

National Academy of Sciences Keck Center
500 Fifth St.
Room 100
Washington, DC
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Workshop agenda

Workshop Proceedings

Workshop Proceedings-in-Brief

 

Workshop Videos

 

View all workshop videos

 

The Committee on Applied and Theoretical Statistics held a two-day workshop on the challenges of applying scientific inference to big data. The workshop brought together statisticians, data scientists and domain researchers from different biomedical disciplines to explore four key issues of scientific inference:

  • Inference about causal discoveries driven by large observational data
  • Inference about discoveries from data on large networks
  • Inference about discoveries based on integration of diverse datasets
  • Inference when regularization is used to simplify fitting of high-dimensional models

 

The aim of the workshop was to identify new developments that hold significant promise and to highlight potential research program areas for the future.  Please contact Michelle Schwalbe at mschwalbe@nas.edu with any questions.

 

Workshop Agenda with Links to Presentations:

 

Wednesday, June 8, 2016

 

8:30-9:40am - Welcome and Overview

Michael Daniels, University of Texas, Austin

Alfred Hero, University of Michigan

Michelle Dunn, National Institutes of Health

Nandini Kannan, National Science Foundation, Division of Mathematical Sciences

10:00am-2:10pm - Session I - Inference about discoveries based on integration of diverse datasets

Alfred Hero, University of Michigan

Andrew Nobel, University of North Carolina, Chapel Hill

Genevera Allen, Rice University

Jeffrey S. Morris, MD Anderson Cancer Center

2:30-5:40pm - Session II - Inference about causal discoveries driven by large observational data

Joe Hogan, Brown University

Elizabeth Stuart, Johns Hopkins University

Sebastien Haneuse, Harvard University

Dylan Small, University of Pennsylvania


Thursday, June 9, 2016

 

8:30-8:40am - Opening Perspectives from Stakeholders

Chaitan Baru, National Science Foundation, Computer and Information Science and Engineering

8:40am-12:30pm - Session III - Inference when regularization is used to simplify fitting of high-dimensional models

Daniela Witten, University of Washington

Michael Kosorok, University of North Carolina at Chapel Hill

Emery Brown, Massachusetts Institute of Technology

Xihong Lin, Harvard University

Jonathan Taylor, Stanford University

1:00-3:00pm - Concluding Panel Discussion

Moderator: Robert Kass, Carnegie Mellon University

Alfred hero, University of Michigan

Bin Yu, University of California, Berkeley

Cosma Shalizi, Carnegie Mellon University

Andrew Nobel, University of North Carolina, Chapel Hill