Faculty
Joe Guinness – Department of Statistics
I am interested in analyzing data observed on spatial and temporal domains, and the theoretical, methodological, and computational issues that commonly arise in environmental and meteorological applications with large datasets. My research is grounded in the spectral theory of random fields, which often provides concise characterizations of statistical models for spatial-temporal data and leads to computationally efficient methodology.
Krishna Pacifici – Department of Forestry and Natural Resources
My specific research interests are driven by understanding the effects of environmental stressors and disturbances on ecological populations and communities and by making conservation and management decisions in the face of such uncertainty. I use a wide variety of statistical modeling tools, but focus on hierarchical Bayesian modeling and spatiotemporal modeling to estimate the influence of biotic and abiotic factors on ecological communities.
Brian Reich – Department of Statistics
My research focuses on developing novel statistical methods to address important environmental problems. My primary applications areas include air pollution, climate and meteorology, ecology, and spatial epidemiology. Some tools I find to be particularly useful in tackling challenging problems in these fields are Bayesian methods, extreme value analysis, high-dimensional techniques, quantile regression, and spatiotemporal modeling.