We are a computational genomics group focused on understanding the genetics, development, and evolution of complex phenotypes and disease. Our research is primarily computational, involving the development and application of techniques that fall within the disciplines of computational statistics and machine learning, although we also complement these efforts with our own experimental research. Current projects include regularized regression techniques for genome-wide association studies (GWAS), probabilistic graphical modeling algorithms for biological network discovery, application of next-generation sequencing data to problems in medical genomics, and combined experimental and computational analysis of developmental network connections inDrosophila. Our research also includes collaborations with groups working in medical, agricultural, and evolutionary fields. Please see our “Research” pages for more information.