Events
Seminar | Institute of Artificial Intelligence for Digital Health
Advancing Health at the Speed of AI
The dream of precision health is to develop a data-driven, continuous learning system where new health information is instantly incorporated to optimize care delivery and accelerate biomedical discovery. In reality, the health ecosystem is plagued by overwhelming unstructured data and unscalable manual processing. Self-supervised AI such as large language models (LLMs) can supercharge structuring of biomedical data and accelerate transformation towards precision health. In this talk, I’ll present our research progress on generative AI for precision health, spanning biomedical LLMs, multi-modal learning, and causal discovery. This enables us to extract knowledge from tens of millions of publications, structure multimodal real-world data for millions of cancer patients, and apply the extracted knowledge and real-world evidence to advancing precision oncology in deep partnerships with real-world stakeholders.
Speaker Bio
Dr. Hoifung Poon is general manager at Health Futures in Microsoft Research and an affiliated professor at the University of Washington Medical School. He leads biomedical AI research and incubation, with the overarching goal of structuring medical data to optimize delivery and accelerate discovery for precision health. His team and collaborators are among the first to explore large language models (LLMs) in health applications, from foundational research to incubations at large health systems and life science companies, and ultimately to productization. He has given tutorials on these topics at top conferences such as theAssociation for Computational Linguistics (ACL), the Association for theAdvancement of Artificial Intelligence (AAAI), and Knowledge Discovery andData Mining (KDD). His research spans a wide range of problems in machine learning and natural language processing (NLP), and his prior work has been recognized with Best Paper Awards from premier venues such as the NorthAmerican Chapter of the Association for Computational Linguistics (NAACL),Empirical Methods in Natural Language Processing (EMNLP), andUncertainty in AI (UAI). He received his PhD in computer science and engineering from the University of Washington, specializing in machine learning and NLP.