JP Gan Professor of Econometrics and Statistics in the Wallman Society of Fellows
Tengyuan Liang is a Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. Professor Liang's research focuses on problems at the intersection of inference, learning, and optimization. He has published in major venues in Economics, Statistics, Applied Mathematics, and Machine Learning. He is a recipient of the National Science Foundation CAREER Award.
He earned a Ph.D. in Statistics from the Wharton School at the University of Pennsylvania and a B.Sc. in Mathematics from Peking University. He was awarded the J. Parker Memorial Bursk Prize and a Winkelman Fellowship from the Wharton School.
His previous work uncovered the presence and effects of implicit regularization in kernel machines, boosting methods, and neural networks in high-dimensional and over-parametrized regimes. He also developed statistical and computational theories for generative models, including generative adversarial networks and probabilistic diffusion models. Additionally, he contributed to the rigorous application of machine learning and optimization techniques in causal inference and uncertainty quantification.
His work appeared in peer-reviewed Economics journals (Econometrica: Journal of the Econometric Society), Statistical journals (The Annals of Statistics, Biometrika, Journal of the Royal Statistical Society: Series B, Journal of the American Statistical Association: Theory and Methods), Applied Mathematics journals (SIAM Journal on Mathematics of Data Science, Information and Inference: a Journal of the IMA), and Machine Learning venues (Journal of Machine Learning Research, Conference on Learning Theory, International Conference on Machine Learning).
He served as an Associate Editor for prestigious journals, including the Journal of the American Statistical Association, and the Operations Research, on the Editorial Board of the Journal of Machine Learning Research, and the Senior Program Committee for the Conference on Learning Theory.
Beyond his role at the University of Chicago, Professor Liang has experience as a Research Scientist at Yahoo! Research in New York, where he worked on large-scale machine learning applications. He also served as a short-term Visiting Professor in Econometrics at the Cowles Foundation for Research in Economics at Yale University.
Machine learning is being tasked with an increasing number of important decisions. But the answers it generates involve a degree of uncertainty.
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Evaluating the performance of machine-learning tools isn’t always easily done.
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