Ziad Obermeyer is an Acting Associate Professor of Health Policy and Management at the UC Berkeley School of Public Health, where he does research at the intersection of machine learning, medicine, and health policy. He previously was an Assistant Professor at Harvard Medical School, where he received the Early Independence Award, the National Institutes of Health’s most prestigious award for exceptional junior scientists. He continues to practice emergency medicine in underserved parts of the US. Prior to his career in medicine, he worked as a consultant to pharmaceutical and global health clients at McKinsey & Co. in New Jersey, Geneva, and Tokyo. See Ziad Obermeyer's website.
Research Expertise and Interest
machine learning, and medicine, health policy
April 21, 2020
In today’s Berkeley Conversations: COVID-19 event, Jennifer Chayes, associate provost of the Division of Computing, Data Science, and Society and dean of the School of Information, spoke with three UC Berkeley experts about how relying on data and algorithms to guide pandemic response may actually serve to perpetuate these inequities — and what researchers and data scientists can do to reverse the patterns.
October 24, 2019
From predicting who will be a repeat offender to who’s the best candidate for a job, computer algorithms are now making complex decisions in lieu of humans. But increasingly, many of these algorithms are being found to replicate the same racial, socioeconomic or gender-based biases they were built to overcome.