My job search experience in Biostatisitcs (2022 winter)
This blog post is written to share my job search experience and some thoughts, which can potentially benefit future faculty candidates. I want to thank the support of my postdoc …
Bingkai Wang
(he/him)
Assistant Professor
Department of Biostatistics, School of Public Health, University of Michigan
Professional Summary
I am a statistical researcher dedicated to developing robust and efficient statistical methods
that enhance clinical research and improve patient health. My 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 and observational studies.
Education and Training
Postdoctoral researcher
2021-2024
University of Pennsylvania
PhD of Biostatistics
2016-2021
Johns Hopkins University
BS of Mathematics
2012-2016
Fudan University
Research Topics
Causal inference
Randomized trials
Large Language Models
Machine learning
Model robustness
Clustered data
Conformal prediction
Stepped-wedge design
Test-negative design
Selected publications
- Bingkai Wang, Michael O. Harhay, Jiaqi Tong, Dylan S. Small, Tim P. Morris, and Fan Li.
On the mixed-model analysis of covariance in cluster-randomized trials. Statistical Science, 41(1), 49–68, 2026. - Bingkai Wang, Fan Li, and Rui Wang.
Handling incomplete outcomes and covariates in cluster-randomized trials: doubly-robust estimation, efficiency considerations, and sensitivity analysis.
Biometrics, in press, 2026. - Fan Li, Jiaqi Tong, Chao Cheng, Xi Fang, Brennan Kahan, and Bingkai Wang.
Model-robust standardization in cluster-randomized trials.
Statistics in Medicine, in press, 2025. - Mengxin Yu, Kendrick Qijun Li, Nicholas Jewell, Eric Tchetgen Tchetgen, Dylan Small, Xu Shi, and Bingkai Wang.
Test-negative designs with various reasons for testing: statistical bias and solution.
Epidemiology, in press, 2025. - Bingkai Wang and Yu Du.
Improving the mixed model for repeated measures to robustly increase precision in randomized trials.
The International Journal of Biostatistics, 20(2), 585–598, 2024. - Bingkai Wang, Chan Park, Dylan S. Small, and Fan Li.
Model-robust and efficient covariate adjustment for cluster-randomized experiments.
Journal of the American Statistical Association, 2024, 1–13. - Bingkai Wang, Xueqi Wang, and Fan Li.
How to achieve model-robust inference in stepped wedge trials with model-based methods?
Biometrics, 80(4), ujae123, 2024. - Bingkai Wang, Suzanne M. Dufault, Dylan S. Small, and Nicholas P. Jewell.
Randomization inference for cluster-randomized test-negative designs with application to Dengue studies: Unbiased estimation, partial compliance, and stepped-wedge design.
The Annals of Applied Statistics, 17(2), 1592–1614, 2023. - Bingkai Wang, Ryoko Susukida, Ramin Mojtabai, Masoumeh Amin-Esmaeili, and Michael Rosenblum.
Model-robust inference for clinical trials that improve precision by stratified randomization and covariate adjustment.
Journal of the American Statistical Association, 118(542), 1152–1163, 2023. - Bingkai Wang, Brian S. Caffo, Xi Luo, Chin-Fu Liu, Andreia V. Faria, Michael I. Miller, and Yi Zhao.
Regularized regression on compositional trees with application to MRI analysis.
Journal of the Royal Statistical Society: Series C, 71(3), 541–561, 2022. - Bingkai Wang, Xi Luo, Yi Zhao, and Brian Caffo.
Semiparametric partial common principal component analysis for covariance matrices.
Biometrics, 77(4), 1175–1186, 2021. - Bingkai Wang, Elizabeth L. Ogburn, and Michael Rosenblum.
Analysis of covariance in randomized trials: More precision and valid confidence intervals, without model assumptions.
Biometrics, 75(4), 1391–1400, 2019.
Blogs
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