Kaixuan obtained training in computational biology and bioinformatics and is currently doing research in statistical genetics and genomics. His research has focused on statistical modeling and machine learning in regulatory genomics and chromatin structure, in particular, the dynamics of genome-wide transcription factor and nucleosome occupancy in both human and yeast. His current research focuses on developing statistical methods for RNA and DNA methylation data, and integrative analysis of genotype and molecular phenotypes. His expertise includes molecular QTL mapping, epigenomics and gene regulation, genetic association studies, chromatin accessibility and 3D chromatin structure, and single-cell genomics. In general, he is passionate in developing statistical and machine learning methods to better understand the mechanisms by which genetic variation impacts gene regulation and diseases. In addition, he is interested in integrating high-throughput multi-omic datasets to gain deeper insights into the disease mechanisms.
