Please note: This seminar will take place in DC 1304.
Daniel McDuff
Senior Staff Research Scientist and Manager, Google
Affiliate Professor, University of Washington
A new era of wearable foundation models that capture rich information about behavior and physiology present the opportunity to discover novel biomarkers of disease and forecast health states into the future. These models, combined with Personal Health Agents, built using agentic AI, will provide new opportunities for people to interface with these data, learn about them in a personalized way, and make healthy choices.
In this talk I will present several of our latest projects on health foundation models (ICLR'25, NeurIPS'25, Nature), differential diagnosis (Nature) and Personal Health Agents (Nature Medicine) that bridge these topics.
Bio: Daniel McDuff is a Senior Staff Research Scientist and Manager at Google and Affiliate Professor at the University of Washington. Daniel completed his PhD at the MIT Media Lab in 2014 and has a B.A. and Masters from Cambridge University. Daniel has published over 200 peer-reviewed papers on health AI (Nature, Nature Medicine), multimodal machine learning (NeurIPS, ICLR, ICCV, ECCV, SIGGRAPH) and human-computer interaction (CHI, CSCW).
His projects have been reported in many publications including The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist, Scientific American and Forbes magazine. Daniel was named a WIRED Innovation Fellow, an ACM Future of Computing Academy member and has spoken at TEDx and SXSW.