Professor of Econometrics and Statistics and William Ladany Faculty Fellow
Tengyuan Liang is a Professor of Econometrics and Statistics at the University of Chicago, Booth School of Business. He is a recipient of the National Science Foundation CAREER Award. He has published in leading venues in Economics, Statistics, Applied Mathematics, and Machine Learning. Professor Liang's research focuses on problems at the intersection of inference, learning, and optimization.
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 work appeared in peer-reviewed Economics journals (Econometrica: Journal of the Econometric Society), Statistical journals (The Annals of Statistics, 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).
His past research has contributed to (i) bridging the empirical and theoretical gap in modern statistical learning, (ii) understanding optimization and inference of over-parametrized or infinite-dimensional statistical models, and (iii) exploring the role of stochasticity in solving non-convex optimization. Currently, he works on (i) generative models (mathematical theory and inference methods), (ii) causal inference (individualization and optimized experimentation), and (iii) overparametrization and regularization (insights and algorithms).
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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