I am a postdoctoral researcher in the Statistics and Data Science Department of the Wharton School, University of Pennsylvania (advised by Dylan Small and Nicholas P. Jewell).
I got my PhD degree from the Biostatistics department at Johns Hopkins Bloomberg School of Public Health (advised by Michael Rosenblum and Brian Caffo).
My research interests are in causal inference, randomized clinical trials, clustered data and their interactions with infectious disease research and computer sciences.
My CV is available here.
[News] I am fortunate to receive the 2024 IMS New Researcher Travel Award.
[News] My work ““Model-robust and efficient inference for cluster-randomized experiments” has been accepted by Journal of American Statistical Association: Theory and Methods.
[News] My work “Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Adjustment for Covariate Adjustment” has been cited by the FDA in their 2023 Guidance for Industry: “Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biologics.”
[News] My K99/R00 application was scored 10 (the best score) in the peer-review phase at NIAID! (My experience is shared in my blog here.) I have started my K99 phase on May 1st, 2023.
[News] I will join the Biostatistics department at the University of Michigan, Ann Arbor as a tenure-track assistant professor in 2024! (My job search experience is shared in my blog here.)
Bingkai Wang, Xueqi Wang, Rui Wang, and Fan Li. (2024) “How to achieve model-robust inference in stepped wedge trials with model-based methods?” arXiv: 2401: 15680.
Bingkai Wang, Fan Li, and Rui Wang. (2024) “Handling incomplete outcomes and covariates in cluster-randomized trials: doubly-robust estimation, efficiency considerations, and sensitivity analysis.” arXiv: 2401.11278.
Bingkai Wang, Fan Li, and Mengxin Yu. (2024) “Conformal causal inference for cluster randomized trials: model-robust inference without asymptotic approximations.” arXiv: 2401.01977.
Bingkai Wang, Chan Park, Dylan Small, and Fan Li. (2023). “Model-robust and efficient inference for cluster-randomized experiments.” Journal of the American Statistical Association, Theory and Methods Section, in press.
Bingkai Wang, Michael O. Harhay, Dylan S. Small, Tim P. Morris, and Fan Li. (2023). “On the robustness and precision of mixed-model analysis of covariance in cluster-randomized trials.” arXiv.
Bingkai Wang, Yu Du. (2021). “Robustly leveraging the post-randomization information to improve precision in the analyses of randomized clinical trials.” International Journal of Biostatistics, 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 covariate adjustment.” Journal of the American Statistical Association, Theory and Methods Section, 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.