Research Expertise and Interest

artificial intelligence, health

Research Description

Adam Yala is an assistant professor in Computational Precision Health, Statistics, and EECS. His lab develops machine learning methods for personalized cancer care and to translate them to clinical practice; his overarching goal is to offer each patient the right intervention (e.g. screening exam or particular treatment choice) at the right time according to their individual risks and preferences.   To this end, the Yala lab focuses on three major themes: 1) modeling full patient records (e.g. multi-modal imaging, pathology, etc) to better predict patient outcomes, 2) deriving better decisions from AI-driven predictors (e.g. screening and treatment policies, choosing therapeutic targets, providing decision quality guarantees, etc.) and 3) clinical translation. His tools are implemented at multiple hospital systems around the world, and underlie prospective clinical trials.