*Assistant Professor
Chicago Booth*

**Tengyuan Liang** works on problems at the intersection of statistical inference, machine learning, and optimization. His main research focuses on the mathematical and algorithmic aspects of inference and learning under computational budgets, especially for large-scale datasets. Specific topics include computational difficulty and efficiency in statistical inference, statistical learning theory, and network science. He is also interested in online learning, stochastic optimization, and applied probability.

His research has been published in such journals as the *Annals of Statistics*, the *Journal of Multivariate Analysis*, and *IEEE Transactions on Network Science and Engineering*, as well as in conjunction with such leading machine learning conferences as the Conference on Learning Theory and the International Conference on Machine Learning. Outside of academia, Liang has experience as a research scientist at Yahoo! Research in New York, collaborating on large-scale machine learning problems with industrial applications.

Liang earned a PhD in statistics from the Wharton School of the University of Pennsylvania, where he received the Winkelman Fellowship and the J. Parker Bursk Memorial Prize for excellence in research. He also holds a BS in mathematics and applied mathematics from Peking University in China.