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Adam Yala is an Assistant Professor in the Department of Statistics at the University of California, Berkeley. His research focuses on developing machine learning methods for personalized cancer care with the overarching goal of providing patients with the right intervention, such as screening exams and particular treatment choices, at the right time, tailored to their individual risks and preferences. His lab explores several major themes, including modeling full patient records, which encompasses multi-modal imaging and pathology, to better understand patient outcomes. Additionally, he works on deriving AI-driven predictors for decision-making in clinical settings, including screening and treatment policies, choosing therapeutic targets, and providing decision guarantees. The tools his lab develops are implemented in multiple hospital systems around the world and are foundational to prospective clinical trials.
The Mathematics Subject GRE is required for the Fall 2026 admissions cycle. General GRE is optional.