Data Science, Statistics, and Health, with a Focus on Statistical Learning and Sparsity and Applications to Biomedicine

Abstract: I will discuss some general issues in the application of statistics to biomedicine. These include the importance of transparency, reproducibility and simplicity. Along the way, the relationship of sparse modelling with deep learning will be discussed. Then I will cover some recent advances in sparse modelling (lasso), including SNPnet for GWAS studies, and the pretrained lasso.

 

Bio: Robert Tibshirani is a Professor of Biomedical Data Science and 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 Signifi cance Analysis of Microarrays (SAM). He also co-authored fi ve 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’. He is an active collaborator with many scientists at Stanford Medical school.
Tibshirani received the COPSS Presidents' Award in 1996. Given jointly by the world's leading statistical societies, the award recognizes outstanding contributions to statistics by a statistician under the age of 40. He was elected a Fellow of the Royal Society of Canada in 2001, the National Academy of Sciences in 2012, and the Royal Society of Britain in 2019. In 2021 he received the ISI Founders of Statistics Prize for his 1996 paper Regression Shrinkage and Selection via the Lasso.