I am an Assistant Professor in the Department of Statistics. My research has four main focuses: (1) elucidating and completing the theory of likelihood-based inference for univariate extremes, (2) developing spatial extremes models that have flexible and realistic dependence properties, (3) implementing these models efficiently for high-dimensional datasets with the help of high-performance computing and (4) developing useful real-life applications in climate science, hydrology, environmental statistics and neuroscience. These research topics range from building mathematical foundations for models to have accurate and flexible tail properties to executing the models on large spatial datasets. My research projects during the past five years have led to both the advancement of extreme value theory and interdisciplinary applications in climate science.
My goal as a researcher is to develop reliable statistical methods and computational strategies that are useful in environmental, ecological, and geophysical applications. I want to utilize the full power of spatio-temporal models to improve our understanding of climate change, to characterize trends in extremes more robustly, and to increase confidence in projections of changes in future environmental statistics. I will also seek to collaborate with researchers in other fields such as neuroscience to increase the visibility of spatial extremes methods and to explore wider applications.