Trevor Hastie

Trevor Hastie is a Stanford statistician who works on statistical learning, the lasso family of regression methods and the software that puts them to use. After a Stanford PhD in 1984, he worked at AT&T Bell Laboratories, where he helped develop the S programming language, and he joined the Stanford faculty in 1994. With his student Hui Zou, he published the elastic net paper in 2005, and he coauthored The Elements of Statistical Learning, An Introduction to Statistical Learning and Statistical Learning with Sparsity. The National Academy of Sciences elected him in 2018, and in 2025 he received the Rao Prize and shared the Founders of Statistics Prize with Zou. He is a professor emeritus at Stanford and maintains the glmnet package for R.

Now at: Stanford University
Born: 1953
Badges: Bridge
Boomerang
Loyal

Years link to their sources.

Timeline

1976–1984

From Rhodes to a Stanford PhD

He earns a B.Sc. (hons) in statistics at Rhodes University in 1976 and an M.Sc. at the University of Cape Town in 1979. At Stanford, he completes a PhD in 1984 under Werner Stuetzle with a dissertation titled Principal Curves and Surfaces.
1986–1994

Bell Labs and the S language

In 1986, he returns to the United States and joins AT&T Bell Laboratories in Murray Hill, New Jersey, where he works until his move to Stanford in 1994. With John Chambers, he co-directs the development of the S programming language. He publishes Generalized Additive Models with Robert Tibshirani in 1990 and Statistical Models in S with John Chambers in 1991.
1994–2013

Professor at Stanford

He joins Stanford in 1994 as an associate professor in statistics and biostatistics and is promoted to full professor in 1999. He chairs the Department of Statistics from 2006 to 2009, and in 2013 he is named the John A. Overdeck Professor of Mathematical Sciences.
2005

The elastic net paper

In 2005, he and his student Hui Zou publish Regularization and variable selection via the elastic net. The method addresses high-dimensional regression by improving prediction accuracy and stabilizing variable selection.
2009–2015

Statistical learning textbooks

In 2009, he publishes the second edition of The Elements of Statistical Learning with Robert Tibshirani and Jerome Friedman. He coauthors An Introduction to Statistical Learning with Gareth James, Daniela Witten and Tibshirani in 2013. Statistical Learning with Sparsity, written with Tibshirani and Martin Wainwright in 2015, covers the lasso, the elastic net and group lasso penalties, and compressed sensing.
2014–2019

Parzen Prize and academy elections

He receives the Emanuel and Carol Parzen Prize for Statistical Innovation in 2014. In 2018, he is elected to the US National Academy of Sciences, and in 2019 he becomes a foreign member of the Royal Netherlands Academy of Arts and Sciences.
2025

Rao Prize and Founders Prize

In 2025, he receives the C. R. and Bhargavi Rao Prize from the Department of Statistics at Penn State University. He and Hui Zou also receive the 2025 Founders of Statistics Prize from the International Statistical Institute for their elastic net paper.
As of October 2026

Emeritus professor and glmnet

He is an emeritus professor of statistics and of biomedical data science at Stanford, with the title John A. Overdeck Professor. He is the maintainer of glmnet, the CRAN package for lasso and elastic-net models, whose version 5.1 appears in September 2026.