He received his Ph.D. in Statistics from Duke University. Dr. Hans’ main research interest is in the development of Bayesian methodology for the analysis of modern, complex datasets. He is particularly interested in the development of related computational methods. Other areas of interest include Markov chain Monte Carlo methods as well as the use of parallel computing in statistics. Specific methodological research areas include the problems of variable selection and model uncertainty in contexts of regression, prediction, and complex multivariate modeling with many variables. A key element of his research is the development of stochastic search and MCMC methods for exploring large model spaces. Recent focus has been on the development of new classes of prior distributions for regression problems that provide connections to penalized optimization procedures.