I am an Assistant Professor in the departments of Statistics and Meteorology at Penn State University, where I conduct fundamental research in statistical climatology. My research involves inferring geophysical processes, such as surface temperatures, from numerous sources of data that each have different uncertainties and different relationships with the underlying process. The observations, as well as the geophysical processes, typically display spatial and temporal dependencies, which motivates the hierarchical statistical approach that is a common thread to my research. Such models allow for the construction of scientifically-informed, space-time relationships for the geophysical process under analysis, and for the dependence of each type of observation on that process. Bayesian inference then allows for a complete treatment of uncertainty, and posterior samples of the geophysical process can be used to answer a wide array of scientific questions.
A major theme of my current research is to modernize the statistical techniques used in the reconstruction of past climate from natural proxies. This work began while I was a graduate student, and later a Research Associate, in the Department of Earth and Planetary Sciences at Harvard University, where I worked with Peter Huybers. My dissertation involved the development of a simple hierarchical model to assimilate various surface temperature proxies (ice cores, tree ring widths and densities, etc.), along with the modern instrumental record, to reconstruct the spatial pattern of past climate. This work was novel in the paleoclimate literature for specifying a parametric space-time covariance model for the surface temperature process, and then specifying both instrumental and proxy observations as functions of this latent process. I am currently working to generalize this model in a number of ways – for example, to include proxy observations that reflect climate on different spatial and temporal scales, or that suffer from temporal uncertainty.
I spent the 2009-2010 academic year as a postdoctoral fellow at SAMSI, where I participated in the Program on Space-time Analysis for Environmental Mapping, Epidemiology and Climate Change. I helped lead the Paleoclimate Working Group and was the lead author on a review paper that discussed the statistical challenges involved in inferring past climate and detailed how hierarchical modeling combined with Bayesian inference can improve upon currently favored approaches. A second thrust of the working group has involved the application of extreme value theory to proxy observations, to explore the temporal and spatial dependencies of the decadal minima and maxima of proxy observations.
I was a postdoctoral fellow with the IMAGe group at NCAR for the 2010-2011 academic year, where I continued my research into the statistics of paleoclimate reconstructions. During my time at NCAR, I became involved in an ongoing project, led by Suz Tolwinski-Ward, to develop and apply a non-linear and bivariate model for tree ring growth for the simultaneous reconstruction of both soil moisture and surface temperatures. A separate project completed at NCAR involved the development of a technique for improving the calculation of climate anomalies from incomplete time series.