Ran Dai

Ran Dai

PhD Student

University of Chicago, Department of Statistics

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.


  • High-dimensional statistical inference
  • High-dimensional shape constrained methods
  • Machine learning


  • MS in Statistics, 2016

    University of Chicago

  • PhD in Medicinal and Pharmaceutical Chemistry, 2015

    University of Minnesota Twin Cities

  • BS in Pharmaceutical Sciences, 2009

    Peking University