Thomas Stopka is an Epidemiologist and Professor with the Department of Public Health and Community Medicine at Tufts University School of Medicine. Through his research, Dr. Stopka explores the interconnectedness of substance use, social and behavioral risk factors, and overdose and infectious disease outcomes among high-risk and often hidden populations through community-engaged, interdisciplinary, multi-methods, applied epidemiological research studies. His major research interests focus on the overlap substance use, infectious disease (HCV, HIV, and STIs), and opioid overdose. He employs qualitative, biostatistical, geographic information systems (GIS), spatial epidemiological, and laboratory approaches in his studies to assess the risk landscape, access to health services, and implement and test public health and clinical interventions to address health disparities. Stopka is currently a multi-Principal Investigator (MPI) on three National Institute on Drug Abuse (NIDA)-funded studies that aim to: 1) Predict future opioid overdoses in Massachusetts employing Bayesian spatiotemporal models to inform pre-emptive public health responses; 2) determine the best timing for extended-release medications (XR-Buprenorphine) for opioid use disorder among incarcerated people in Massachusetts; and 3) assess the effectiveness of a mobile telemedicine-based hepatitis C treatment intervention among rural people who inject drugs. He is also a Co-Investigator on the National Institute of Health (NIH)-funded HEALing Communities Study to reduce opioid overdose deaths in Massachusetts, in which he is leading GIS and spatial epidemiological analyses. These and other studies that Stopka is working on employ: 1) ethnographic and qualitative approaches to assess contextual factors tied to salient exposures and outcomes of interest and to generate hypotheses; 2) innovative epidemiological, legal, and policy scans to assess substance use-related morbidity and mortality and health services landscape; 3) spatiotemporal methods to explore the distribution of measures that affect risk, and to determine the geolocation of and access to current services, as well as gaps; and, 4) Bayesian spatiotemporal dynamic modeling approaches to inform small area forecasting of opioid-related mortality.