School of Public Health
Twin Cities
This research focuses on the development of precision medicine, causal inference, and statistical learning methodology for the analysis of complex observational studies. The researchers are particularly interested in addressing various forms of population heterogeneity with the aim of improving patient health outcomes. Work in this area has involved applications in health system risk modeling and in personalizing health system intervention enrollment decisions. The research also includes methodological and computational developments with the aim of flexibly modeling highly complex and/or large-scale data. This involves data analysis of large health-related datasets and developing statistical methodology to analyze them. In developing statistical methodology, large-scale simulation studies are conducted to assist in understanding the operating characteristics of the methodology.