My research interests are mainly in Bayesian spatial statistics, with applications in the environmental sciences. As remote-sensing instruments mounted on satellites have made it possible to collect massive amounts of data on a global scale, much of my research focuses on the development of complex, flexible spatial methods that can be applied to big global datasets in a computationally feasible way. For example, I work with collaborators at NASA and NCAR on combining measurements from several satellites measuring CO2 on a global scale, on how to run related algorithms in parallel in modern distributed-computing environments, and on the real-time analysis of massive, streaming spatio-temporal datasets that are important for forecasting severe rainfall.
