Skip to main content

Refining the Concept of Scientific Inference When Working with Big Data Proceedings of a Workshop (2017) / Chapter Skim
Currently Skimming:

Appendix B: Workshop Agenda
Pages 96-99

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 96...
... 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 96
Read the entire page →
From page 97...
... 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
Read the entire page →
From page 98...
... Lunch 1:00 Concluding Panel Discussion Moderator: Robert Kass, Carnegie Mellon University
Read the entire page →
From page 99...
... Appendix B 99 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
Read the entire page →

Key Terms

  • cancer center
  • comparative effectiveness
  • health record
  • effectiveness research
  • robert kass

  • anderson cancer
  • elizabeth stuart
  • concluding panel
  • health records
  • using electronic

  • panel discussion
  • record data
  • discoveries driven
  • causal discoveries
  • harvard university

  • carnegie mellon
  • mellon university
  • discuss comparative
  • electronic health
  • research using

  • data using
  • observational data
  • causal inference
  • johns hopkins
  • brown university

  • session ii
  • md anderson
  • adjourn day
  • hopkins university


    • This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
    More information on Chapter Skim is available.