Yang Ni

Yang Ni works at the intersection of statistics, artificial intelligence, biology and health. His research develops statistical methods for causal discovery, Bayesian model-based clustering, and reinforcement learning, with applications to multi-omics and electronic health records.

He was previously an Associate Professor at Texas A&M University, where he received the Research Impact Award and the Faculty Excellence Award. At Texas A&M, he co-directed the CPRIT-funded Single Cell Data Science Core and the Center for Statistical Bioinformatics. He currently serves as an Associate Editor for the Journal of the American Statistical Association, Bayesian Analysis, and The American Statistician, and leads several federally funded projects. He was a postdoctoral fellow at the University of Texas at Austin and earned his Ph.D. in Statistics from Rice University.