Ziad Obermeyer is an Assistant Professor at Harvard Medical School and a practicing emergency physician at the Brigham and Women’s Hospital, both in Boston.
His work uses machine learning to solve critical problems in clinical medicine. As patients get older and more complex, the volume of health data grows exponentially, and it becomes harder and harder for the human mind to keep up. Dr. Obermeyer’s work is focused on applying machine learning to find hidden signals in health data, and help doctors make better decisions and drive innovations in clinical research.
He is a recipient of an Early Independence Award from the NIH Common Fund, and a faculty affiliate at ideas42, Ariadne Labs, the Institute for Quantitative Social Science at Harvard University. He holds an A.B. (magna cum laude) from Harvard and an M.Phil. from Cambridge, and worked as a consultant at McKinsey & Co. in Geneva, New Jersey, and Tokyo, before returning to Harvard for his M.D (magna cum laude).