Dr. Mathew Kiang is an Assistant Professor in the Department of Epidemiology and Population Health at Stanford University School of Medicine. Dr. Kiangreceived his doctorate from the Department of Social and Behavioral Sciences at Harvard TH Chan School of Public Health. Before that, he received his MPH at New York University, his BA in Sociology from San Diego State University, he was a 2016 fellow at University of Chicago’s Data Science for Social Good summer fellowship, and a Fellow at the Harvard FXB Center for Health and Human Rights.

Dr. Kiang's research lies at the intersection of computational social science and social epidemiology. Methodologically, his work revolves around combining disparate data sources in epidemiologically meaningful ways. For example, he works with individual-level, non-health data (e.g., GPS, accelerometer, and other sensor data from smartphones), traditional health data (e.g., survey, health systems, or death certificate data), and third-party data (e.g., cellphone providers or ad-tech data). To do this, he uses a variety of methods such as joint Bayesian spatial models, traditional epidemiologic models, dynamical models, microsimulation, and demographic analysis.

Substantively, Dr. Kiang's work focuses on socioeconomic and racial/ethnic inequities in health. A few current projects include:

1.  K99/R00-funded work focused on reducing racial/ethnic inequities in the treatment of opioid use disorder;
2. work with CrisisReady on quantifying the impacts of wildfires, natural disasters, climate change, and power outages on medically-vulnerable populations to inform equitable disaster response; and
3. work with the COVID-19 Mobility Data Network on leveraging third-party data to evaluate and inform public health interventions to the COVID-19 pandemic.