I’m an associate professor and Blue Cross of California Distinguished Professor at the Berkeley School of Public Health. My research and teaching focus on the intersection of machine learning and health.

I’m interested in 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).

I’m cautiously optimistic about machine learning, but I’ve also showed 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.

In a previous research life, I worked in global health—developing new methods to quantify adult mortality, and showing that war kills more people than most people believe.

I’m a co-PI of a lab, joint between Berkeley and U Chicago, that builds algorithmic tools to improve decision-making and deepen understanding in health.

I co-founded Nightingale Open Science, a non-profit that makes massive new medical imaging datasets available for research—to read more, see our comment in Nature Medicine, and media coverage in the FT, Wired, and Stat). I also co-founded Dandelion, a data platform to jump-start AI innovation in health.

I’m a Chan Zuckerberg Biohub Investigator, a Faculty Research Fellow at the National Bureau of Economic Research, and was named an Emerging Leader by the the National Academy of Medicine. Before Berkeley, I was an Assistant Professor at Harvard Medical School. I trained in emergency medicine, and still love practicing (here).

A short CV is here. Bios of various lengths, talk abstracts, disclosures, and high resolution picture are here.

Contact: zobermeyer at berkeley edu* | Twitter: @oziadias

*I’m so sorry if I don’t respond to an email—please don’t hesitate to resend it. I struggle to stay on top of my inbox, and often fail.