Biography

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 will join the Biostatistics department at the University of Michigan, Ann Arbor as a tenure-track assistant professor! (My job search experience is shared in my blog here.)

[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.)

Causal inference for randomized clincial trials

• Bingkai Wang, Chan Park, Dylan Small, and Fan Li. (2022). “Model-robust and efficient inference for cluster-randomized experiments.” arXiv.
• Bingkai Wang, Suzanne M. Dufault, Dylan S. Small, Nicholas P. Jewell. (2022). “Randomization Inference for Cluster-Randomized Test-Negative Designs with Application to Dengue Studies: Unbiased estimation, Partial compliance, and Stepped-wedge design.” Annals of Applied Statistics, in press.
• Bingkai Wang, Michael O. Harhay, Dylan S. Small, Tim P. Morris, and Fan Li. (2021). “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.” arXiv.
• 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, in press.
• 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.

Statistical methods for brain imaging

• Bingkai Wang, Brian S. Caffo, Xi Luo, Chin-Fu Liu, Andreia V. Faria, Michael I. Miller, and Yi Zhao. (2022) “Regularized regression on compositional trees with application to MRI analysis.” Journal of the Royal Statistical Society: Series C (Applied Statistics), 71(3): 541- 561.
• Bingkai Wang, Xi Luo, Yi Zhao, and Brian Caffo. (2021) “Semiparametric partial common principal component analysis for covariance matrices.” Biometrics, 77(4): 1175-1186.

Awards

• Election to membership of the Phi Beta Kappa Society (honor for excellence in scholarship), 2021.
• Best student paper runner-up, ASA Biopharmaceutical Section, 2021.
• Margaret Merrell Award (awarded to one doctoral student per year for outstanding research), Johns Hopkins University Department of Biostatistics, 2021.
• Distinguished student paper award, ENAR International Biometric Society, 2021.
• Student paper award, the Statistical Meeting in Imaging, 2020.
• Center of Excellence in Regulatory Science and Innovation (CERSI) Scholarship, U.S. Food and Drug Administration and Johns Hopkins University, 2017-2021.
• Shanghai outstanding undergraduate student (for top 1% senior-year undergraduate students), 2016.
• Fudan University undergraduate research fellowship, 2015-2016.
• National Scholarship (for top 1% undergraduate students in China per year), 2014-2015.
• Shanghai Scholarship (for top 5% undergraduate students in Shanghai), 2013.

Invited Commentary Articles

• Bingkai Wang, Ryoko Susukida, Ramin Mojtabai, Masoumeh Amin-Esmaeili, Michael Rosenblum. (2021). Comment: Inference after covariate-adaptive randomisation: aspects of methodology and theory. Statistical Theory and Related Fields.
• Michael Rosenblum and Bingkai Wang. (2019) “The Critical Role of Statistical Analyses in Maximizing Power Gains From Covariate-Adaptive Trial Designs.” JAMA network open 2(4): e190789-e190789.

Invited talks

• Model-robust and efficient inference for cluster-randomized experiments, Society for Clinical Trials Annual Meeting, May 2023.
• Randomization Inference for Cluster-Randomized Test-Negative Designs with Application to Dengue Studies, Scientific meeting of the World Mosquito Program, February 2022.
• Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Adjustment for Additional Baseline Variables.
  • ICSA Applied Statistics Symposium, September 2021
  • Novartis Statistics Seminar, September 2021
  • JSM, August 2021
  • Johns Hopkins University Biostatistics Departmental Seminar, September 2020
  • ENAR, March 2020
• Semiparametric Partial Common Principal Component Analysis for Covariance Matrices. SMI, May 2020.