Ran Dai is a fourth-year PhD candidate in Statistics at the University of Chicago advised by Rina Foygel Barber. Ran’s research interest is in high dimensional inference, nonparametric modeling, and shape-constrained regressions, multiple testing, and causal inference. In particular, Ran is interested in developing computationally efficient algorithms for these problems with statistical convergence guarantee and valid inference; and applying these methods towards real datasets in the areas of drug development, HIV mutation study and cancer study.
MS in Statistics, 2016
University of Chicago
PhD in Medicinal and Pharmaceutical Chemistry, 2015
University of Minnesota Twin Cities
BS in Pharmaceutical Sciences, 2009