Trevor Hastie

TREVOR HASTIE The John A. Overdeck Professor, Professor of Statistics, Professor of Biomedical Data Science, Emeritus, Stanford University

Welcome to my home page. I held a joint appointment in the Department of Statistics at Stanford University, and the Department of Biomedical Data Science in the Stanford School of Medicine from 1994-2025. Before that I was a member of the technical staff at AT&T Bell Laboratories, Murray Hill, New Jersey, where I worked for 9 years. In 2018 I was elected to the United States National Academy of Sciences. I am a dual citizen of the United States and South Africa. For more details, click on the link to my biography.

2025 Rao Prize

Trevor Hastie was awarded the C.R. and Bhargavi Rao prize for Fundamental Contributions to Statistical Theory and Practice. The award took place at Penn State University on May 20, 2025 at the Rao prize conference.

Ten Statistical Ideas that Changed the World

Trevor Hastie and Rob Tibshirani interview authors of seminal papers in the field of Statistics. This is part of a project from Stanford's Stat 319 class taught by Rob Tibshirani in Winter 2024 to discuss important papers in the field. Click on the image to see the videos. Visit the class website to find the original papers, presentation slides, and summaries.

Statistical Learning with Python

Online course with Rob Tibshirani and Jonathan Taylor. This is a version of our earlier course, but is based on the new book "Statistical Learning, with Applications in Python" (see the book description pane). In this course the chapter lectures are as before, but the lab chapters are new. Here is a link to the youtube playlist for the Python lab lectures. Here is a link to the interviews in the course.

Introduction to Statistical Learning, with Applications in Python

Published July 5, 2023. Available from Springer.
This book differs from ISLR Ed2 (listed below) in two important ways:

The lab section of each chapter is implemented in Python. The resources link on the book homepage www.statlearning.com provides jupyter notebooks of these labs as well.
Jonathan Taylor is a fifth coauthor of the book (and a Python expert).

Interview with Sir David Cox (7/15/1924 - 1/18/2022) in Fall 2021

This 20 minute interview with the 97 year old legend by Rob Tibshirani and me was recorded for the second edition of our free online course on Statistical Learning, and appears in the chapter on Survival Analysis. David Cox was at home in Oxford, and the recording was done via Zoom with us in a Stanford recording studio.

Statistical Learning with R

Online course with Rob Tibshirani. Updated January, 2022 with new modules for additional chapters in second edition of Introduction to Statistical Learning. Includes new videotaped interviews with David Cox, Geoff Hinton and Yoav Benjamini. Hosted by edX in self-paced mode. See link for details of course and certification. Link to Youtube playlist for all 105 videos.

Introduction to Statistical Learning, with applications in R (2nd edition)

Published August 1, 2021. Available in eprint from Springer. Orders can be placed for hardcover, available August 30, 2021. Three additional chapters in additional 179 pages:

Deep Learning
Survival Analysis and Survival Data
Multiple Testing

glmnet 4.0

Released May 2020 and on CRAN. Major addition is full GLM family functionality. Any legitimate GLM family object can be passed as the family argument to glmnet, over and above the built-in (and more computationally efficient) families which are specified by character strings. All the new features of glmnet 3.0 apply, including relaxed lasso and elastic net, software for model assessment, functions for building the X matrix that can deal with NAs and factor inputs, a progress bar for fitting big models, and more.

Sigillum Magnum from University of Bologna

Received this seal of the university in September 2019.

Computer Age Statistical Inference with Bradley Efron

Cambridge University Press, August 2016. Interview with the authors at the launch of the book in 2017 at the Joint Statistical Meetings in Baltimore.

Interview with Jon Gurstelle for Statistics Views, November 2016

Statistical Learning with Sparsity, with Rob Tibshirani and Martin Wainwright.

Chapman and Hall, May 2015.

Website Created by Tim Hastie