David L. DeMets Lecture Series - November 2017

Robert Tibshirani

The third annual David L. DeMets Lectures Series presented by the Department of Biostatistics & Medical Informatics will be held on November 9th and 10th of 2017.

November 2017 Speaker

Rob Tibshirani

Professor of Statistics and Biomedical Data Science
Stanford University


Robert Tibshirani's main interests are in applied statistics, biostatistics, and data mining. He is co-author of the books Generalized Additive Models (with T. Hastie), An Introduction to the Bootstrap (with B. Efron), and Elements of Statistical Learning (with T. Hastie and J. Friedman). His current research focuses on problems in biology and genomics, medicine, and industry. With collaborator Balasubramanian Narasimhan, he also develops software packages for genomics and proteomics.


Some Progress and Challenges in Biomedical Data Science

Thursday, November 9th - 3:30 to 4:45pm
1335 HSLC

I will discuss some new developments in the application of statistics and data science to medicine, and some challenges that this exciting field faces. Examples from my own work that I will discuss include cancer diagnosis from DESI mass spec data, estimating the number of units of platelets needed by a hospital each day and making treatment recommendations from observational data (electronic health records).

Watch the live stream here!


Recent Advances in Post-Selection Statistical Inference

Friday, November 10th - 12:00 to 1:00pm
Biotechnology Center Auditorium

In this era of big data and complex statistical modeling, scientists use sophisticated computational tools to search through a large number of models, looking for meaningful patterns. The challenge is then to judge the strength of a large number of apparent associations that have been found. This statistical problem has become known as “Post-selection inference,” the assessment of significance and effect sizes from a data-set after mining the same data to find these associations. In this talk I will discuss new methods for computing p-values and confidence intervals in regression, that correctly account for the adaptive selection of the model.

This is joint work with Jonathan Taylor, Ryan Tibshirani, Will Fithian and Richard Lockhart

Watch the live stream here!

Publications of Interest