معرفی
Xiang Zhou is a Professor of Statistics and Data Science at Yale University and an Adjunct Professor of Biostatistics at the University of Michigan. He holds a Ph.D. in Neurobiology and an M.S. in Statistics from Duke University (2010), and a B.S. in Biology from Peking University (2004). His research spans statistical genetics, genomics, and machine learning, with a focus on developing computational methods for high-dimensional genetic data.
Research Interests: Dr. Zhou specializes in Bayesian methods, spatial statistics, and deep learning for genomic applications. His work includes genome-wide association studies (GWAS), single-cell RNA sequencing, spatial transcriptomics, and integrative modeling of multi-omics data. Key areas include genetic prediction, causal inference, and scalable algorithms for large-scale genomic datasets.
Publication Trends: His recent articles emphasize spatial transcriptomics, Mendelian randomization, and non-parametric modeling, published in high-impact journals like Nature Biotechnology and Nature Methods. These works advance methodologies for spatial gene expression analysis, cell-type deconvolution, and genetic architecture inference.
Labs & Teams: He leads the Zhou Lab at Yale, which focuses on genomic data science and develops open-source software (e.g., GEMMA, SPARK) for the research community.
