Ziad Obermeyer

Ziad Obermeyer is the Blue Cross of California Distinguished Associate Professor of Health Policy and Management at the UC Berkeley School of Public Health, where he co-leads the Computational Precision Health program with the University of California, San Francisco. A physician and researcher, Obermeyer works at the intersection of machine learning and health, investigating how algorithms can improve medical decision-making and examining algorithmic bias in healthcare systems. His research explores both the opportunities and challenges of applying artificial intelligence to medicine, contributing to ongoing discussions about responsible AI implementation in clinical settings.

Before joining UC Berkeley, Obermeyer was an assistant professor at Harvard Medical School, where he received the Early Independence Award, the National Institutes of Health's award for exceptional junior scientists. He has been named an Emerging Leader in Health and Medicine by the National Academy of Medicine and has received recognition from academic societies and research institutions. In addition to his academic work, Obermeyer maintains clinical practice as an emergency medicine physician in underserved areas of the United States, grounding his research in real-world medical challenges. Prior to his medical career, he worked as a consultant to pharmaceutical and global health clients at McKinsey & Company in multiple international offices.

Obermeyer's research focuses on how machine learning can support clinical decision-making and reveal new mechanisms of disease, while critically examining how algorithms can reinforce bias if deployed without careful scrutiny. He is a Chan Zuckerberg Biohub Investigator and a faculty research fellow at the National Bureau of Economic Research. His work bridges technical innovation and clinical application, contributing to the advancement of medicine through artificial intelligence and data science.