My research has largely been concerned with developing quantitative methods that seek to characterize variation in large-scale genomic studies. Often the goal of these studies is to identify a particular type of signal, for example, genes with expression levels that are associated with disease status. I will describe some of my group’s major research themes aimed at the problem of identifying relevant signals in genomic data in the presence of complex sources of “noise.” This involves false discovery rates, latent variable modeling, and empirical Bayes methods, which are all active research topics at the interface of statistics and machine learning.
John Storey received his PhD in statistics from Stanford University. He has been a faculty member at UC-Berkeley and University of Washington, and he is currently a professor at Princeton University. He is known for developing methods for high-dimensional data, particularly with applications to genomics. Storey is an elected fellow of the American Association for the Advancement of Science (AAAS) and the Institute of Mathematical Statistics (IMS).