Biography

I am a postdoctoral fellow 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 in Johns Hopkins Bloomberg School of Public Health (advised by Michael Rosenblum and Brian Caffo).

My research interests are in causal inference, randomized clinical trials, and precision medicine.

My CV is available [here].

News

• [Sept 2021] My paper “Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Covariate Adjustment” [link] is accepted for publication at Journal of American Statistical Association, Theory and Method Section. Big thanks to Michael and my collaborators!
• [Sept 2021] I give an invited talk at the Novartis Statistics Seminar. [Slides]
• [Mar 2021] I am elected to membership of the Phi Beta Kappa Society.
• [Feb 2021] I win the second place in the ASA Biopharmaceutical Section’s student paper competition.
• [Feb 2021] I successfully defend my thesis titled “Statistical Methods for Analyzing Randomized Trials and Brain Imaging Data”. [Slides]
• [Dec 2020] I win the ENAR 2021 distinguished student paper award.

Causal inference for randomized clincial trials

• Bingkai Wang, Ryoko Susukida, Ramin Mojtabai, Masoumeh Amin-Esmaeili, and Michael Rosenblum. (In press) “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. (2019).
• 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, no. 4 (2019): 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. “Regularized regression on compositional trees with application to MRI analysis.” arXiv (2021).
• Bingkai Wang, Xi Luo, Yi Zhao, and Brian Caffo. “Semiparametric partial common principal component analysis for covariance matrices.” Biometrics (2020).

Awards

• Second best student paper, American Statistical Association (ASA) Biopharmaceutical Section, 2021.
• Margaret Merrell Award (awarded to one doctoral student per year for outstanding research), Department of Biostatistics, Johns Hopkins University, 2021.
• Distinguished student paper award, Eastern North American Region (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.

Invited talks

• Semiparametric Partial Common Principal Component Analysis for Covariance Matrices. SMI, May 2020.
• 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

Invited Commentary Articles

• Bingkai Wang, Ryoko Susukida, Ramin Mojtabai, Masoumeh Amin-Esmaeili, Michael Rosenblum. Comment: Inference after covariate-adaptive randomisation: aspects of methodology and theory. Statistical Theory and Related Fields (2021).

• Michael Rosenblum and Bingkai Wang. “The Critical Role of Statistical Analyses in Maximizing Power Gains From Covariate-Adaptive Trial Designs.” JAMA network open 2, no. 4 (2019): e190789-e190789.