Colloquium Details

Learning to Assess Disease and Health In Your Home

Speaker: Yuzhe Yang, MIT

Location: 60 Fifth Avenue Room 150

Date: March 19, 2024, 2 p.m.

Host: Sumit Chopra


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 methods to bridge the persistent gaps in medical 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 the potential of AI to realize passive, longitudinal, and in-home tracking of disease severity, progression, and medication response.

Speaker Bio:

Yuzhe Yang is a Ph.D. candidate at MIT. 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. His work has been deployed in hospitals including MGH and Mayo Clinic, and in patients’ homes for passive health monitoring, and has been adopted in clinical trials for drug and biomarker discovery across various diseases.


In-person attendance only available to those with active NYU ID cards.

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