Faculty Profile
Bingkai Wang, PhD
- Assistant Professor, Biostatistics
Dr. Wang is a statistical researcher dedicated to developing robust and efficient
statistical methods that enhance clinical research and improve patient health. His
work focuses on leveraging modern analytical tools, including causal inference and
machine learning, to address key challenges in the design and analysis of clinical
trials.
- PhD, Biostatistics, Johns Hopkins University, 2021
- BS, Mathematics, Fudan University, 2016
Research Interests
Causal inference, randomized trials, machine learning, AI
Research Projects
Efficient inference in stepped wedge trials; Leveraging Large language models in causal inference; Empirical evaluation of covariate adjustment methods in randomized trials.
Causal inference, randomized trials, machine learning, AI
Research Projects
Efficient inference in stepped wedge trials; Leveraging Large language models in causal inference; Empirical evaluation of covariate adjustment methods in randomized trials.
Bingkai Wang, Chan Park, Dylan Small, and Fan Li. (2023). “Model-robust and efficient inference
for cluster-randomized experiments.” Journal of American Statistical Association:
Theory and Methods, in press.
Bingkai Wang, Ryoko Susukida, Ramin Mojtabai, Masoumeh Amin-Esmaeili, and Michael Rosenblum. (2021). “Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Adjustment for Covariate Adjustment.” Journal of American Statistical Association: Theory and Methods, 118(542): 1152-1163.
Bingkai Wang, Elizabeth L. Ogburn, and Michael Rosenblum. (2019). “Analysis of covariance in randomized trials: More precision and valid confidence intervals, without model assumptions”. Biometrics, 75(4): 1391-1400.
Bingkai Wang, Ryoko Susukida, Ramin Mojtabai, Masoumeh Amin-Esmaeili, and Michael Rosenblum. (2021). “Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Adjustment for Covariate Adjustment.” Journal of American Statistical Association: Theory and Methods, 118(542): 1152-1163.
Bingkai Wang, Elizabeth L. Ogburn, and Michael Rosenblum. (2019). “Analysis of covariance in randomized trials: More precision and valid confidence intervals, without model assumptions”. Biometrics, 75(4): 1391-1400.
Areas of Expertise: Biostatistics, Clinical Trials, COVID-19, Precision Health