Dr. Han’s research mainly focuses on two areas (1) statistical methodology (Bayesian approach or computational optimization algorithms) development for inference of graphical models, especially for directed acyclic graphs (DAG’s) which is a convenient and very popular tool to decode and visualize complex relations among variables. Graphical models have wide applications in many fields such as biological science (e.g. pathway analysis), social science, economics and so on; (2) the high throughput genomic technologies are growing rapidly and huge amount of “big-omic” data are being generated. It’s challenging to leverage multi-scale multiple-level data sets to advance the understanding of the mechanisms of human diseases. My interest is to develop statistical and computational tools to analyze/integrate high throughput -omics data to better understand the mechanism of complex human traits.
