Dr. Andrew Brouwer is a computational epidemiologist and modeler. He is an Assistant Research Scientist in the Department of Epidemiology at the University of Michigan. He earned his PhD in Applied and Interdisciplinary Mathematics at the University of Michigan in 2015, and he also holds an MA in Statistics and an MS Environmental Science and Engineering. Dr. Brouwer’s research focuses on mathematical and statistical modeling for inference in epidemiology, with the goal of making the most of dynamic data for the benefit of public health. He is an expert in methods that address questions of parameter identifiability, estimation, and uncertainty. Informed by his interdisciplinary background, his research spans a growing constellation of application areas including infectious disease, cancer, and tobacco control. Though these areas may seem disparate at first glance, Dr. Brouwer’s work is united by the use of mechanistic frameworks as a lens to interpret real data, a focus on longitudinal patterns and dynamics, and by a quantitative toolset that includes differential equation models, multistate transition models, and age–period–cohort models, among others.