This will be a world-class research talk in biostatistics or biomedical informatics.
Talk 1: Some Progress and Challenges in Biomedical Data Science
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).
Talk 2: Recent Advances in Post-Selection Statistical Inference
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
For additional information on Dr. Tibshirani, please see:
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.