Tengyuan Liang works on problems at the intersection of inference, learning, and optimization. Specifically, Liang's primary research centers around the following topics: (1) bridging the empirical and theoretical gap in modern statistical learning; (2) understanding the computational and algorithmic aspects of statistical inference; (3) exploring the role of stochasticity in solving non-convex optimization.
Liang's research has appeared in journals such as The Annals of Statistics, Econometrica, the Journal of the Royal Statistical Society, the Journal of the American Statistical Association, and the Journal of Machine Learning Research, as well as in leading peer-reviewed machine learning venues such as the Conference on Learning Theory (COLT), and the International Conference on Machine Learning (ICML), among others.
Outside of the University of Chicago, Liang has experience as a research scientist at Yahoo! Research at New York in 2016, collaborating on large-scale machine learning problems with industrial applications. As a short-term visiting faculty in econometrics, Liang visited the Cowles Foundation for research in economics at Yale University in 2019. He is currently on the editorial board of the Journal of Machine Learning Research and on the senior program committee for the Conference on Learning Theory.
Liang 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 China in 2012. He joined the Chicago Booth faculty in 2017.
2020 - 2021 Course Schedule
||Econometrics and Statistics Colloquium