Abdullah Al-Mamun

Abdullah Al-Mamun is an Assistant Professor in the Department of Pharmaceutical Systems and Policy at the School of Pharmacy, West Virginia University (WVU). He holds a PhD in Computing and Information Sciences from the University of Northumbria, UK, and completed postdoctoral training at Cornell University and Yale University.

• PhD, University of Northumbria, Newcastle, UK
• Postdoctoral Fellowship, Epidemiology of Microbial Diseases, Cornell University
• Postdoctoral Fellowship, Yale School of Public Health

His research centers on health outcomes data science, integrating machine learning, mathematical modeling, and epidemiological methods to address complex health challenges. Key areas include medication complexity in critically ill patients, polysubstance use in the opioid epidemic, and rare disease diagnosis delays. His lab, HealBig (Health Outcomes and BigData Informatics), develops predictive tools using large-scale datasets from EHRs, state surveillance, and national surveys.

The recent publications reflect a strong trend in applying data science to clinical and public health problems, particularly focusing on acute kidney injury, polypharmacy, and infectious disease dynamics. These studies employ advanced methodologies such as machine learning, agent-based modeling, and statistical time-series analysis to derive actionable insights.

Dr. Al-Mamun has taught graduate courses including PHAR 786, PHAR 769, and PHAR 788, focusing on health services research and data methods. He mentors several doctoral students and leads interdisciplinary research teams involving clinicians, pharmacists, and data scientists.

His scientific contributions are supported by active research projects funded through WVU, with applications in clinical decision support, public health policy, and precision medicine. Though no formal awards are listed, his work appears in reputable journals such as Journal of Clinical Medicine, PLOS ONE, and Proceedings of the Royal Society B.

The HealBig lab continues to expand its scope in rare diseases like Transthyretin Amyloid Cardiomyopathy (ATTR-CM) and Merkel Cell Carcinoma (MCC), aiming to develop early prediction models and reduce diagnostic delays through data-driven approaches.