Nick Seewald develops statistical methodology for the design and analysis of studies involving complex longitudinal data to answer questions about health policy and precision health. Specifically, he focuses on methods that aid in the construction of decision rules which specify for whom to provide what treatment and when. He takes a broad approach to this, seeking to make an impact in both statistics and domain sciences from a project’s inception to the dissemination of results.

Currently, he is a postdoctoral fellow in the Department of Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health, working on causal inference for health policy evaluation.