Dr. Hans’ research focuses on the development of Bayesian methodology for the analysis of modern, complex data. He is particularly interested in approaches to regression modeling with many predictors. Dr. Hans’ work includes the development of computational methods for modeling in these settings, including Markov chain Monte Carlo methods and the use of distributed computing in statistical modeling.
PhD, Statistics, Duke University
MS, Statistics, Duke University
AB, Statistics, Harvard University
National Science Foundation