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Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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B

Workshop Agenda

JUNE 8, 2016

8:30 a.m. Welcome and Overview
Introductions from the Co-Chairs
Michael Daniels, University of Texas at Austin
Alfred Hero, University of Michigan
Perspectives from Stakeholders
Michelle Dunn, National Institutes of Health
Nandini Kannan, National Science Foundation, Division of Mathematical Sciences
Overview of the Workshop
Michael Daniels, University of Texas at Austin
9:40 Break
10:00 Session I - Inference About Discoveries Based on Integration of Diverse Data Sets

Presenter: Alfred Hero, University of Michigan, to speak about integrating and drawing inferences from multimodal data

Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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Discussant: Andrew Nobel, University of North Carolina at Chapel Hill

Q&A
11:45 Lunch
12:45 p.m. Session I, continued

Presenter: Genevera Allen, Rice University, to speak about statistical methods using medical/health case studies

Discussant: Jeffrey S. Morris, MD Anderson Cancer Center

Q&A
2:10 Break
2:30 Session II - Inference About Causal Discoveries Driven by Large Observational Data

Presenter: Joseph Hogan, Brown University, to speak about causal inference and decision making with health record data using a case study on HIV in Kenya

Discussant: Elizabeth Stuart, Johns Hopkins University

Q&A
3:55 Break
4:15 Session II, continued

Presenter: Sebastien Haneuse, Harvard University, to discuss comparative effectiveness research using electronic health records

Discussant: Dylan Small, University of Pennsylvania

Q&A
5:40 Adjourn Day 1
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
×

JUNE 9, 2016

8:30 a.m. Opening Perspectives from Stakeholders
Chaitan Baru, National Science Foundation, Computer and Information Science and Engineering
8:40 Session III - Inference When Regularization Is Used to Simplify Fitting of High-Dimensional Models

Presenter: Daniela Witten, University of Washington, to discuss network reconstruction from high-dimensional ordinary differential equations

Discussant: Michael Kosorok, University of North Carolina at Chapel Hill

Q&A
10:00 Break
10:20 Session III, continued

Presenter: Emery Brown, Massachusetts Institute of Technology, to speak about using different recording methods with high-dimensional time series

Discussant: Xihong Lin, Harvard University

Q&A
Technical/Methodological Presenter
Jonathan Taylor, Stanford University
Q&A
12:30 p.m. Lunch
1:00 Concluding Panel Discussion

Moderator: Robert Kass, Carnegie Mellon University

Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
×

Panelists: Alfred Hero, University of Michigan Bin Yu, University of California, Berkeley Cosma Shalizi, Carnegie Mellon University Andrew Nobel, University of North Carolina at Chapel Hill

3:00 Adjourn Workshop
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
×
Page 96
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
×
Page 97
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
×
Page 98
Suggested Citation:"Appendix B: Workshop Agenda." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
×
Page 99
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The concept of utilizing big data to enable scientific discovery has generated tremendous excitement and investment from both private and public sectors over the past decade, and expectations continue to grow. Using big data analytics to identify complex patterns hidden inside volumes of data that have never been combined could accelerate the rate of scientific discovery and lead to the development of beneficial technologies and products. However, producing actionable scientific knowledge from such large, complex data sets requires statistical models that produce reliable inferences (NRC, 2013). Without careful consideration of the suitability of both available data and the statistical models applied, analysis of big data may result in misleading correlations and false discoveries, which can potentially undermine confidence in scientific research if the results are not reproducible. In June 2016 the National Academies of Sciences, Engineering, and Medicine convened a workshop to examine critical challenges and opportunities in performing scientific inference reliably when working with big data. Participants explored new methodologic developments that hold significant promise and potential research program areas for the future. This publication summarizes the presentations and discussions from the workshop.

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