Abstract

Human physiology is becoming a new frontier for general-purpose AI. Unlike text and images, physiological signals are continuous, multimodal, and tightly coupled with both short-term dynamics and long-term health outcomes. In this talk, I will present our recent work on sleep foundation models and language-grounded understanding of sleep physiology. I will then discuss wearable foundation models and benchmarks with the goal of learning generalizable representations from large-scale real-world sensing data for personal health. Finally, I will cover health reasoning and agentic AI for physiological time series, and a broader vision for AI systems that can interpret, reason over, communicate about, and support action on human physiology.

bio: Yuzhe Yang is an Assistant Professor of Computational Medicine and Computer Science at UCLA , where he directs the Health Intelligence Lab. He is also a visiting faculty researcher at Google. He received his Ph.D. in Computer Science at MIT . His research interests include machine learning, artificial intelligence, and their applications in science, medicine, and human health. His research has been published in Nature, Nature Medicine, NeurIPS, ICML , and ICLR , featured in media outlets such as WSJ , Forbes, and BBC , and recognized by the AMIA Doctoral Dissertation Award and Forbes 30 Under 30.