Ran Dai, PhD, MS
Education
- 2020 PhD Statistics, University of Chicago
- 2016 MS Statistics, University of Chicago
- 2015 PhD Medicinal Chemistry, University of Minnesota, Twin Cities
- 2009 Pharmaceutical Sciences, Peking University
Research Interests
Dr. Dai's main research interest is in high dimensional, nonparametric and shape constrained statistical inference, machine learning and multiple testing. She has 5 years of data analysis experience with projects in: nonparametric statistics, shape constrained regression, FDR control, and collaborations in different applied areas including drug development, clinical trial and geology.
Selected Publications
- R. Dai, H. Song, G. Raskutti and R. F. Barber, (2020) The bias of isotonic regression. Electronic Journal of Statistics. 14: 801-874
- C. Zheng, R. Dai, R. P. Gale, M. J. Zhang, (2019) Causal inference in randomized clinical trials. Bone Marrow Transplantation. 1-5
- C. Zheng, R. Dai, P. Hari and M. J. Zhang. (2017) Instrumental variables with competing risk models. Statistics in medicine. 36: 1240-1255
- F. Liu, S. Dawadi, K. Maize, R. Dai, et al. (2017) Structure-Based Optimization of Pyridoxal 5’-Phosphate-Dependent Transaminase Enzyme (BioA) Inhibitors that Target Biotin Biosynthesis in Mycobacterium tuberculosis. Journal of Medicinal Chemistry, 60: 5507-5520.
- R. Dai, R. F. Barber, (2016) The Knockoff filter for FDR control in group-sparse and multitask regression. Proceedings of the 33rd international conference on Machine Learning (ICML).
Professional Affiliations
- American Statistical Association
- Institute of Mathematical Statistics