Developing cutting-edge statistical and machine learning methods to unlock biological insight from genetic and genomic data
Xiang Zhou’s research centers on genomic data science, to 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 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, the 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.
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Biography
Zhou is a Professor in the Department of Statistics and Data Science at Yale University. He earned a BS in Biology from Peking University and an MS in Statistics and a PhD in Neurobiology from Duke University. After postdoctoral training at the University of Chicago, he joined the Department of Biostatistics at the University of Michigan in 2014 and served in leadership roles in Precision Health and AI and Digital Health Innovation. He joined Yale University in 2025. His research focuses on developing advanced statistical and machine learning methods, including AI tools, for the analysis of large-scale genomic and multi-omics data.