Department of Statistics and the College
Xinran Li is a statistician whose focus is on developing novel methodologies for causal inference. He specializes in randomization-based inference for causal effects, sensitivity analysis for observational studies, experimental design such as rerandomization, and Bayesian inference. He also applies these methodologies to social and biomedical sciences, and explores new avenues to apply and advance these methodologies.
His research has been published in Annals of Statistics, Biometrika, Journal of the American Statistical Association, Journal of the Royal Statistical Society: Series B, and Proceedings of the National Academy of Sciences. He has also received the New World Gold Medal for Best Doctoral Thesis in the Mathematical Sciences, the Best Paper Gold Award from the International Consortium of Chinese Mathematicians, the Outstanding Young Researcher Award from the International Chinese Statistical Association, and the National Science Foundation CAREER award.
Li received a BS in mathematics and applied mathematics and a BA in economics from Peking University, and a PhD in statistics from Harvard University. He was a postdoctoral researcher in the Department of Statistics at the University of Pennsylvania. Most recently, he was an assistant professor in the Department of Statistics at the University of Illinois Urbana-Champaign.