Dr. Tengyuan Liang

Biography

Tengyuan Liang is a Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His research focuses on the intersection of inference, learning, and optimization, with publications in leading journals in economics, statistics, machine learning, and applied mathematics. He has received the National Science Foundation CAREER Award and earned his Ph.D. in Statistics from the Wharton School at the University of Pennsylvania, where he also completed his B.Sc. in Mathematics. Liang has received several prestigious awards, including the J. Parker Memorial Bursk Prize and the Winkelman Fellowship from the Wharton School. His previous work has uncovered the presence effects of implicit regularization in kernel machines, boosting methods, and neural networks in high-dimensional over-parametrized regimes. He has developed statistical and computational theories for generative models, generative adversarial networks, and probabilistic diffusion models, as well as PDE-based stochastic samplers. Additionally, he has contributed to the rigorous application of machine learning optimization techniques in causal inference and uncertainty quantification. Liang has served as an Associate Editor for prominent journals, including the Journal of the American Statistical Association and Operations Research, and has been a part of the Editorial Board for the Journal of Machine Learning Research. He has worked as a Research Scientist at Yahoo! Research in New York, focusing on large-scale machine learning applications, and held a position as a short-term Visiting Professor in Econometrics at the Cowles Foundation for Research in Economics at Yale University.

Research Interests

Courses