Ran Dai, PhD, MS
Assistant Professor, UNMC Department of Biostatistics
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
- Dai, R., Li, R., Lee, S. and Liu, Y., (2024). Controlling false discovery rate for mediator selection in high-dimensional data. Biometrics, 80(3), p.ujae064.
- Dai, R. & Zheng, C. (2023) False discovery rate-controlled multiple testing for union null hypotheses: a knockoff-based approach. Biometrics, 79, 3497–3509.
- Dai, R., Zheng, C., & Zhang, M. J. (2023). On High-Dimensional Covariate Adjustment for Estimating Causal Effects in Randomized Trials with Survival Outcomes. Statistics in Biosciences, 15(1), 242-260.
- Dai, R., Ma, J., Wu, M., Mai, Y., & He, W. (2023). A Flexible Ensemble Learning Method for Survival Extrapolation. Therapeutic Innovation & Regulatory Science, 57(3), 580-588.
- Dai, R., Song, H., Barber, RF., Raskutti, G. Convergence guarantee for the sparse monotone single index model. Electronic Journal of Statistics, 16(2), 4449-4496, (2022)
Professional Affiliations
- American Statistical Association
- Institute of Mathematical Statistics