91ÁÔÆæ

Real-World Data Researcher Tiansheng Wang Joins PHOP Faculty

September 5 — The 91ÁÔÆæ College of Pharmacy recently welcomed pharmacoepidemiologist Tiansheng Wang, Pharm.D., Ph.D., as an assistant professor in the Department of Pharmaceutical Health Outcomes and Policy.

After completing his Ph.D. in epidemiology at the University of North Carolina at Chapel Hill (UNC-Chapel Hill), Wang continued as a postdoctoral scholar under the mentorship of leading researcher Til Stürmer, M.D., MPH, Ph.D., for three additional years. During this time, he developed a causal machine learning subgrouping algorithm and its high-dimensional extension to uncover treatment effect heterogeneity – a methodology for pinpointing which patients are more likely benefit from, or be harmed by, a given therapy.

Prior to his Ph.D. studies, Wang earned his Doctor of Pharmacy (Pharm.D.) and M.S. in pharmaceutical sciences from North Dakota State University. He then worked as a full-time community pharmacist for Rite Aid in Washington state before joining the Department of Pharmacy Administration and Clinical Pharmacy at Peking University in China as a lecturer, while also practicing part-time as a hospital pharmacist in Beijing. Wang also holds a bachelor’s degree in pharmaceutical sciences from Shenyang Pharmaceutical University in China.  

Wang’s research centers on evaluating the safety and effectiveness of medications by developing and applying causal inference and machine learning techniques to large-scale administrative health databases. He has authored or coauthored 80 peer-reviewed publications, including multiple papers in leading methodological and clinical journals, such as the American Journal of Epidemiology, and Diabetes Care.

He has presented more than two dozen oral and/or poster presentations at annual meetings of organizations including the International Society for Pharmacoepidemiology, the Alzheimer’s Association, the American Chemical Society, and the American Society of Human Genetics. In addition, he has delivered nearly two dozen invited talks at institutions across the United States and China.

Wang’s recent work focuses on developing AI- and machine learning- powered causal inference methods—including high-dimensional iterative causal forests, temporal high-dimensional propensity scores, and multimodal data (genetic, imaging, and real-world claims) approaches—to improve confounding control in drug effects assessments and identify tailored therapies.

He received the American Diabetes Association Precision Medicine Postdoctoral Fellowship Award to explore individualized therapy in older adults with type 2 diabetes and has been selected for the New Investigator Award from the Alzheimer’s Association and National Alzheimer's Coordinating Center (to be formally announced in September) for his research utilizing neuroimaging data to identify drug repurposing candidates for Alzheimer’s disease.

Wang has received several honors, including the Merck-Guess Scholarship in Pharmacoepidemiology and science awards from the Chinese Pharmaceutical Association. He has served as an editorial consultant for the American Journal of Epidemiology and is an active reviewer for numerous high-impact journals, including JAMA Internal Medicine, Diabetes Care, and The BMJ.

—&²Ô²ú²õ±è;Naqiyah Kantawala