THESIS DEFENSE: Machine Learning Approaches for Healthcare Discovery, Delivery, and Equity

Abstract: Today's clinical systems frequently exhibit delayed diagnoses, sporadic patient visits, and unequal access to care. Can we identify chronic diseases earlier, potentially before they manifest clinically? Furthermore, can we bring comprehensive medical assessments into patient’s own homes to ensure accessible care for all? In this talk, I will present machine learning approaches to bridge the persistent gaps in healthcare discovery, delivery, and equity. I will first introduce an AI-powered digital biomarker that detects Parkinson’s disease multiple years before clinical diagnosis, using just nocturnal breathing signals. I will then discuss a simple self-supervised framework for contactless measurement of human vital signs using smartphones. Finally, I will discuss principled methods to achieve equitable healthcare decision-making systems across diverse subpopulations and distribution shifts for real-world deployment.

Committee Members: Dina Katabi (advisor, MIT), Marzyeh Ghassemi (MIT), Daniel McDuff (Google & University of Washington)

Bio: Yuzhe Yang is a Ph.D. candidate at MIT, advised by Dina Katabi. His research interests include machine learning and AI for human diseases, health and medicine. His research has been published in Nature Medicine, Science Translational Medicine, NeurIPS, ICML, and ICLR, and featured in media outlets such as WSJ, Forbes, and BBC. He is a recipient of the Rising Stars in Data Science, and PhD fellowships from MathWorks and Takeda.