Ziad Obermeyer, Associate Professor
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Ziad Obermeyer, Associate Professor

Ziad Obermeyer works at the intersection of machine learning and health. His research focuses on how machine learning can help doctors make better decisions (like whom to test for heart attack), and help researchers make new discoveries—by ‘seeing’ the world the way algorithms do (like finding new causes of pain that doctors miss, or linking individual body temperature set points to health outcomes). He has also shown how widely-used algorithms affecting millions of patients automate and scale up racial bias. That work has impacted how many organizations build and use algorithms, and how lawmakers and regulators hold AI accountable.
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At the UC Berkeley School of Public Health, Ziad Obermeyer holds the Blue Cross of California Distinguished Associate Professorship in Health Policy and Management. He does research at the nexus of health policy, medicine, and machine learning. The National Institutes of Health’s most prestigious honor for outstanding junior scientists, the Early Independence honor, was given to him while he was an assistant professor at Harvard Medical School. He is still an emergency physician in underprivileged areas in the United States. He was employed by McKinsey & Co. in New Jersey, Geneva, and Tokyo as a consultant for pharmaceutical and global health companies before beginning his medical profession.

  • MD – Harvard Medical School
  • MPhil – Cambridge
  • BA – Harvard College

2023

Obermeyer’s broad research mainly focuses on the nexus between machine learning and health, including large-scale data analysis and supporting physicians in their decision-making. According to Obermeyer’s latest study, there is a cost difference between Black and White patients with similar demands as a result of Black patients’ uneven access to healthcare. These two groups of patients are categorized as having different priorities, and algorithms are able to identify this difference and assign a lower priority to Black patients.

Obermeyer set out to correct this biased proxy mechanism known as “label choice bias” in order to address these amplified racial inequalities and clear the path for access to more egalitarian care. “We can make algorithms work for the better by retraining them to predict less biased proxies, which will reduce disparities rather than widen them and reallocate resources to those who need them,” Obermeyer wrote in an email.


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