• Assistant Professor of Statistics
• Ph.D., Applied Mathematics, University of Southern California, 2004

Dr. Zhang's research interests are in computational biology including genomic sequence analysis, gene evolution, and population-based genetic studies. In particular, he has been focusing on developing statistical models and computational algorithms involving MCMC sampling techniques and Bayesian inference.

His recent research has focused on mapping genes that are responsible for human complex diseases, using genotyped SNP data from unrelated individuals. One direction of research has been to develop Bayesian models coupled with MCMC techniques to identify disease associated genetic factors (e.g. SNPs) and possible interactions between them. An important step to increase the discovery power is to explicitly account for the correlation between SNPs and between individuals, based on theories of population evolution. In addition, Dr. Zhang has been developing methods to delineate both fine-scale genetic variation patterns and large-scale genomic aberrations using experimental data. The goal is to develop computational tools to help understanding evolution, identifying functional elements, and inferring mechanisms of gene regulation in biology.

Last updated: September 18, 2006