Evaluating and improving health equity and fairness of polygenic scores

Dr. Tianyu Zhang is a postdoctoral researcher at Carnegie Mellon University (CMU), Department of Statistics & Data Science. He obtained his undergraduate degree, double-majored in Biology and Mathematics, from Peking University, and Doctor of Philosophy degree in Biostatistics from University of Washington. As a statistician with scientific training, his methodological research strives to achieve both statistical validity and real-world feasibility. His work has been published in high-impact journals across many fields including statistics, medicine and computational biology. Currently, Dr. Zhang works closely with Dr. Kathryn Roeder at CMU on developing novel inferential methods that can be applied to high-dimensional problems including genetic and single-cell data analysis. 

Bernie Devlin is a Professor of Psychiatry and Clinical and Translational Science at the University of Pittsburgh School of Medicine. His research has three major foci: the development or refinement of statistical methods for the analysis of genetic data; the implementation of such methods to discover the genetic basis of disease and related phenotypes; and placing these findings in neurobiology. Much of his empirical work characterizes the genetic basis and neurobiology of autism spectrum disorder and schizophrenia. In 2009 he was inducted as a Fellow of the Statistics Section of the American Association for the Advancement of Science (AAAS) for contributions to modeling genetic data.