Our work broadly falls into two (often overlapping) areas.

Social epidemiology. We investigate the way the structures of society permeate all aspects of health and result in health inequities with the goal of identifying equitable policies and approaches to improving health among structurally and medically vulnerable populations

Computational epidemiology. Rooted in social theory, we use computational methods to rigorously analyze and combine data from non-health data sources (such as mobile phone data or social media data) and traditional source (such as claims or mortality data) to understand large-scale human behavior and its impact on health.

We love using new methods and working with exciting data, but ultimately we are a solutions-driven lab focused on applied problems. In pursuit of this mission, we use a variety of computational (e.g., machine learning, microsimulations), demographic (e.g., decomposition, matrix projection models, lifetable techniques), epidemiologic (e.g. dynamical models), and statistical tools (e.g., Bayesian spatial models) that are most appropriate for the research question.