Professor and Chair; Ph.D., Purdue, 1980.

My general interest is the implementation of Bayesian analysis in complex settings, with particular attention to geophysical problems. The Bayesian paradigm provides opportunities for the combination of physical reasoning and observational data in a coherent analysis framework, but in a fashion that manages the uncertainties in both information sources. A key to the modeling is the hierarchical viewpoint in which separate statistical models are developed for the physical variables studied and the observations conditional on those variables. Modeling physical variables in this way enables incorporation of scientific models across a spectrum of levels of intensity ranging from qualitative use of physical reasoning to strong reliance on numerical models. Modeling and computational methods are being developed and applied to problems of assessing climate change and its impacts, weather forecasting, glacial dynamics, and medium-range climate prediction.