Md Mahmudul Hasan

Md Mahmudul Hasan is an Assistant Professor in the Department of Pharmaceutical Outcomes and Policy at the University of Florida (UF), with a joint appointment in the Department of Information Systems and Operations Management. He also serves as Assistant Director and Faculty Lead of the Medical Marijuana Research Repository under the Florida-funded Consortium for Medical Marijuana Clinical Outcomes Research. His affiliations include the UF Center for Drug Evaluation and Safety (CoDES) and Center for Addiction Research and Education (CARE). Appointed through UF’s AI initiative, his research focuses on AI-driven clinical decision support systems and prescriptive models to address substance use disorders, opioid-related adverse events, and chronic disease management.

Dr. Hasan holds a PhD from Northeastern University’s Decision Analytics Lab and completed a postdoctoral fellowship at the FDA’s Center for Drug Evaluation and Research (CDER). His work integrates machine learning, statistical modeling, and management science to improve healthcare decision-making, with notable projects funded by the CDC and Massachusetts Department of Public Health. His research has been featured in media outlets including NPR, The Main Street Daily News, and Medical Press.

His interdisciplinary studies emphasize opioid use disorder (OUD), predicting treatment discontinuation, and mitigating biases in AI models. He explores telehealth’s role in OUD treatment outcomes and develops frameworks to reduce opioid diversion through secure disposal programs. Dr. Hasan’s methodological contributions include causality-aware machine learning models and fairness-aware algorithms for healthcare applications.

Teaching responsibilities include courses on pharmaceutical policy, managerial statistics, and decision analytics. His research has generated 15+ peer-reviewed publications since 2015, spanning journals like Computers in Biology and Medicine, Addiction, and Journal of the American College of Cardiology. Current projects aim to advance AI ethics in healthcare and optimize resource allocation for chronic disease management.