My main area of research interest is the development of Bayesian methodology for the analysis of modern, complex datasets. I am particularly interested in the development of computational methods to address these problems. Particular 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 this 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.
You can find information about Bayesian activities and research across the world at www.bayesian.org.
Complex Experiments and High-Input Simulators: Challenges in Design, Prediction and Sensitivity
National Science Foundation grant DMS-1310294
PI: Tom Santner
Co-PIs: Angela Dean, Chris Hans
Knowledge-Driven Bayesian Regression
National Science Foundation grant DMS-1007682
Co-PI: Steven MacEachern
High-Dimensional Regression Modeling via Distributed Computing
National Science Foundation grant DMS-0706948