Tengyuan Liang is a Professor of Econometrics and Statistics at The University of Chicago. Professor Liang’s research focuses on problems at the intersection of inference, learning, and optimization. He is the recipient of the National Science Foundation CAREER Award. His research has appeared in journals such as Econometrica, The Annals of Statistics, the Journal of the Royal Statistical Society, the Journal of the American Statistical Association, the Journal of Machine Learning Research, and in leading peer-reviewed machine learning venues such as the Conference on Learning Theory (COLT), the International Conference on Machine Learning (ICML), among other outlets. His current research aims to: (1) bridge the empirical and theoretical gap in modern statistical learning; (2) understand optimization and inference of over-parametrized or infinite-dimensional statistical models; (3) explore the role of stochasticity in solving non-convex optimization.

Outside the University of Chicago, Professor Liang has experience as a Research Scientist at Yahoo! Research in New York in 2016, working on large-scale machine learning applications. Liang visited the Cowles Foundation for Research in Economics at Yale University in 2019 as a short-term Visiting Professor in Econometrics. He is currently on the Editorial Board of the Journal of Machine Learning Research and the Senior Program Committee for the Conference on Learning Theory.

He earned a Ph.D. in Statistics from the Wharton School at the University of Pennsylvania in 2017 and a B.Sc. in Mathematics and Applied Mathematics from Peking University in 2012. He was awarded the J. Parker Memorial Bursk Prize and a Winkelman Fellowship from the Wharton Schoool. He joined the Chicago Booth faculty in 2017.