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.

About the speaker
Hoifung Poon

Hoifung Poon

General Manager, Microsoft Health Futures at Microsoft

Hoifung Poon is General Manager at Health Futures in Microsoft Research and an affiliated faculty 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. His research produces popular open-source foundation models such as PubMedBERT, BioGPT, BiomedCLIP, LLaVA-Med. He has led successful research partnerships with large health providers and life science companies, creating AI systems in daily use for applications such as molecular tumor board and clinical trial matching.

He has given tutorials on these topics at top AI conferences such as ACL, AAAI, and KDD, and his prior work has been recognized with Best Paper Awards from premier AI venues such as NAACL, EMNLP, and UAI. He received his PhD in Computer Science and Engineering from the University of Washington, specializing in machine learning and NLP.



Sessions: April 2nd – 3rd 2024
Trainings: April 15th – 19th 2024


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