Xiang Zhou, PhD

Our research centers on genomic data science, where we develop cutting-edge statistical and machine learning (ML) methods, including deep learning and artificial intelligence (DL/AI) tools, to empower the effective analysis of large-scale, high-dimensional genetic and genomic studies.

Key methodological areas include mixed-effects models, spatial statistics, causal inference, mediation analysis, Bayesian methods including nonparametrics, kernel methods, graphical models, mixture models, continuous and discrete latent variable models, Poisson process and Gaussian process models, integrative modeling, statistical computing, approximate and scalable inference, and, more recently, deep learning.

Key application areas include genome-wide association studies (GWAS), transcriptome wide association studies (TWAS), molecular quantitative trait loci (QTL) mapping studies such as expression QTL (eQTL) and methylation QTL (mQTL) mapping studies, and various functional genomic studies such as chromatin immunoprecipitation sequencing (ChIPseq), bulk RNA sequencing (RNAseq), single cell RNAseq (scRNAseq), bisulfite sequencing (BSseq), and, more recently, spatial omics studies.

By developing novel analytic methods for state-of-the-art genetic and genomic techniques, our goal is to extract key biological insights from these data, advancing our understanding of how genomic variation influences biological functions and contributes to phenotypic variation in various human diseases and disease related complex traits.