Ryan Tibshirani


Ryan Joseph Tibshirani is a professor and chair of the Department of Statistics at the University of California, Berkeley. His work spans high-dimensional statistics, nonparametric estimation, distribution-free inference, convex optimization, and epidemic tracking and forecasting.
WWW stat.berkeley.edu/~ryanti...

Early life and education

Tibshirani was born on December 15, 1985 in Toronto, Canada.
He earned a B.S. in Mathematics from Stanford University in 2007 and a Ph.D. in Statistics from Stanford in 2011; his dissertation, The Solution Path of the Generalized Lasso, was advised by Jonathan Taylor.

Career

From 2011 to 2022, Tibshirani was a faculty member in the Department of Statistics and Department of Machine Learning at Carnegie Mellon University. He joined UC Berkeley in 2022 and became department chair effective July 1, 2025.
Tibshirani is a principal investigator with the Delphi Research Group, which develops public health surveillance and forecasting systems in partnership with the Centers for Disease Control and Prevention.
In 2024, he became founding co-Editor-in-Chief of Foundations and Trends in Statistics with Rina Foygel Barber.

Research

Tibshirani’s research focuses on methodology and theory for high-dimensional and nonparametric problems, often connecting statistical inference with convex optimization. He has made important contributions to regularization and sparsity methods, including the lasso, generalized lasso, and trend filtering, developing both theoretical guarantees and efficient algorithms. He has also contributed to selective inference, to distribution-free predictive inference and to epidemic modeling and forecasting.

Awards and honors

Personal life

Ryan Tibshirani is the son of statistician Robert Tibshirani with brother Charlie Tibshirani and younger sister Julie Tibshirani, who is a co-creator of the R package Generalized Random Forest package. He is married to Jessica Tibshirani and they have two children.

Selected publications

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