My research is motivated by scientific questions about complex systems. In many cases, existing scientific knowledge about the system is available, sometimes represented in a deterministic computer model, and this knowledge needs to be appropriately incorporated into the statistical inference. One such project involves comparing sources of variability in regional climate model experiments. These computer models of the climate system have been developed to provide high-resolution simulations over limited areas, and their boundary conditions are often provided by lower-resolution, global climate models. Using a Bayesian functional ANOVA model, I am exploring to what degree, and in which regions, the global-scale forcing contributes to the overall variability in these regional models.

Much of my applied work concerns environmental and climate problems, and my theoretical interests lie primarily in the realm of spatial statistics. In particular, I am interested in developing good estimators and predictors for spatial processes. When the data are large, the usual likelihood methods become computationally infeasible.

I have developed estimators which are computationally more efficient and shown that they share desirable asymptotic properties with the MLE. I continue to be interested in the properties of so-called "plug-in prediction" for spatial fields, in which one uses the same data both to estimate the covariance structure of the spatial field and then to predict values of the field at unknown locations.