Binhuan Wang, PhD

Department of Population Health, New York University, New York, USA

Prof. Binhuan Wang got his B.S. in Mathematics from Tsinghua University, M.S. in Statistics from Sichuan University, and Ph.D. in Statistics from Georgia State University. His Ph.D. dissertation focused on the statistical evaluation of continuous-scale diagnostic tests with missing data.

His current research focuses on three main areas: (1) complicate data analysis in medical research; (2) statistical methods in variable selection and big/massive data analysis; (3) statistical methods in medical diagnosis. Specifically, at NYU Health, he actively collaborates with physician-researchers from various departments. He leads statistical analyses for multiple NIH or VA funded studies, including randomized control trials and observational studies, as well as developing procedures to address study design, longitudinal data analysis, missing data problems, causal inference, meta-analysis, functional data from fMRI, and imaging data.

Also, he is working on various statistical theories, including model selection, support vector machine, clustering/biclustering, massive data analysis and online learning as well as their applications in health care data. In medical diagnosis, he is interested in the development of statistical methodology in receiver operating characteristic (ROC) analysis, missing data problems, personalized medicine, and population based epidemiological studies.