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.
