Professor Robert Tibshirani, Stanford University
Online through zoom
RSVP for Prof Tibshirani’s Lecture
Title: Data Science, Statistics, and Health with a Focus on Statistical Learning and Sparsity
Abstract: First I will discuss some general issues in the application of statistics to biomedicine. These include the importance
of transparency, reproducibility and simplicity. Then I will cover some recent advances in sparse modelling (lasso), including SNPnet for GWAS studies, and Cooperative learning, a new method for supervised multiview analysis.
of transparency, reproducibility and simplicity. Then I will cover some recent advances in sparse modelling (lasso), including SNPnet for GWAS studies, and Cooperative learning, a new method for supervised multiview analysis.
Bio: Robert Tibshirani is a Professor of Biomedical Data Science, and of Statistics, at Stanford University. He has made important contributions to the statistical analysis of complex datasets. Some of his most well-known contributions are the Lasso, which uses L1 penalization in regression and related problems, generalized additive models and Significance Analysis of Microarrays (SAM). He also co-authored five widely used books ‘Generalized Additive Models’, ‘An Introduction to the Bootstrap’, ‘The Elements of Statistical Learning’, “An Introduction to Statistical learning”, and ‘Sparsity in Statistics: the Lasso and its generalizations