Dr. Wang’s research interests focus on theory and application of biostatistics, behavioral epidemiology, epidemiological methods, and genetic epidemiology/statistical genomics. He has considerable expertise in using SAS and SAS callable SUDAAN, SPSS/AMOS, and R packages in the analyses of large datasets in clinical trials, national surveys, genetic and genomic studies, and multi-omics data. Furthermore, he has contributed to the development and/or application of advanced statistical methods such as linear and non-linear mixed effect models, Poisson regression, longitudinal data analysis, survival models, multivariate data analysis [such as cluster analysis, factor analysis, principal component regression, latent class analysis, structural equation modeling (SEM), and machine learning (ML) and deep learning (DL) tools], meta-analysis, and Bayesian methods.