Yuzhe Yang

Bio: Yuzhe Yang is a PhD candidate in computer science at MIT. He received his B.S. with honors in EECS from Peking University. His research interests include machine learning, and AI for human disease, health and medicine. His works on AI-enabled biomarkers for Parkinson’s disease were named as Ten Notable Advances in 2022 by Nature Medicine, and Ten Crucial Advances in Movement Disorders in 2022 by The Lancet Neurology. His research has been published in Nature Medicine, Science Translational Medicine, NeurIPS, ICML, ICLR, CVPR, and UbiComp. His works have been recognized by the MathWorks Fellowship, Takeda Fellowship, Baidu PhD Scholarship, and media coverage from MIT Tech Review, Wall Street Journal, Forbes, BBC, The Washington Post, etc.

Talk Title: Learning to Assess Disease and Health At Your Home

Abstract: The future of healthcare lies in delivering comprehensive medical services to patients in their own homes. As the global population ages and chronic diseases become increasingly prevalent, objective, longitudinal and reliable health assessment at home becomes crucial for early detection and prevention of hospitalization. Advances in machine learning and smart sensors hold immense potential for transforming in-home healthcare. However, enabling these technologies for at-home clinical applications requires addressing several challenges, including discovering effective biomarkers with accessible vitals, learning from real-world sparse and biased health data, and making ML algorithms reliable for deployment across diverse environments and populations.

In this talk, I will present new learning methods with everyday devices for in-home healthcare that address these challenges. I will first describe a simple self-supervised framework for remote human vitals sensing just using daily smartphones. I will then introduce an AI-powered digital biomarker for Parkinson’s disease that detects the disease, estimates its severity, and tracks its progression using nocturnal breathing signals. Furthermore, I will showcase the potential of AI-based in-home assessment for various diseases and human health sensing, enabling remote monitoring of health-related conditions, timely care and enhancing patient outcomes.