My research develops machine learning methods for personalized cancer care and to translate them to clinical practice; our 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, our lab focuses on three major themes: 1) modeling full patient records (e.g. multi-modal imaging, pathology, etc) to better understand patient outcomes, 2) deriving better decisions from AI-driven predictors (e.g. screening and treatment policies, choosing therapeutic targets, providing decision guarantees, etc.) and 3) clinical translation. Our tools are implemented at multiple hospital systems around the world, and underlie prospective clinical trials.