Dr. Wenbo Wu is a tenure-track Assistant Professor in the Department of Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health, with a forthcoming joint appointment in the Department of Biostatistics. He joined Johns Hopkins in June 2025 following a national search focused on AI and data science in population health, health systems, and risk stratification.

Dr. Wu's research lies at the intersection of statistical methodology, machine learning, causal inference, optimization, and artificial intelligence. His work is distinguished by its strong focus on health equity, healthcare delivery science, and data-driven clinical decision-making. He develops advanced analytic methods to analyze large, complex datasets drawn from electronic health records, administrative claims, disease registries, and randomized controlled trials. His methodological interests span deep learning, natural language processing, double/debiased machine learning, ensemble learning, and meta-learning. These tools are applied to critical public health challenges, including provider profiling with an equity lens, social determinants of health, caregiving burdens, neighborhood disadvantage, and racial and ethnic segregation. Clinically, his work has addressed Alzheimer's disease and related dementias, chronic kidney disease, and disparities in organ donation and transplantation.

Prior to joining Johns Hopkins, Dr. Wu was an Assistant Professor at NYU in Biostatistics, Nephrology, and Data Science. He earned his PhD in Biostatistics and Scientific Computing from the University of Michigan in 2022.