Hoifung Poon
Fri 03 Aug 2018, 11:00 - 12:30
Informatics Forum (IF-4.31/4.33)

If you have a question about this talk, please contact: Diana Dalla Costa (ddallac)


The advent of big data promises to revolutionize medicine by making it more personalized and effective. But big data also presents a grand challenge of information overload, making it difficult to discern knowledge from data, and separate signal from noise. AI can play a key role in translating big data to optimal medical decisions. In particular, deep learning has emerged as a versatile tool for diverse tasks such as machine reading and disease progression modeling. However, like other supervised methods, deep learning requires annotated examples, which are expensive and time-consuming to produce at scale. In this talk, I'll present Project Hanover, where we overcome the annotation bottleneck by combining deep learning with probabilistic logic, and by exploiting indirect supervision from readily available resources such as ontologies and existing databases. This enables us to extract knowledge from millions of publications, reason efficiently with the resulting knowledge graph by learning neural embeddings of biomedical entities, and apply the learned embeddings as powerful features to personalized cancer drug combinations.


Hoifung Poon is the Director of Precision Health NLP and leads Project Hanover at Microsoft Research, with the overarching goal of advancing machine reading for precision health, by combining probabilistic logic with deep learning. He has given tutorials on this topic at top conferences such as the Association for Computational Linguistics (ACL) and the Association for the Advancement of Artificial Intelligence (AAAI). 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 North American Chapter of the Association for Computational Linguistics (NAACL), Empirical Methods in Natural Language Processing (EMNLP), and Uncertainty in AI (UAI). He received his PhD in Computer Science and Engineering from University of Washington, specializing in machine learning and NLP.