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AI for Good Seminar Series - AI for Healthcare

February 10, 2020 - 4:30pm to 5:20pm
William R. Hewlett Teaching Center (Room 200) - 370 Serra Mall, Stanford, CA 94305


AI for Healthcare

Improving health requires targeting and evidence. Marzyeh tackles part of this puzzle with machine learning. This session will cover some of the novel technical opportunities for machine learning in health challenges, and the important progress to be made with careful application to domain.

Marzyeh Ghassemi - Assistant Professor, Faculties of Computer Science & Medicine, University of Toronto and Vector Institute faculty member holding a Canadian CIFAR AI Chair and Canada Research Chair.

Professor Ghassemi has a well-established academic track record in personal research contributions across computer science and clinical venues, including KDD, AAAI, MLHC, JAMIA, JMIR, JMLR, Nature Translational Psychiatry, and Critical Care. She is an active member of the scientific community, on the Board of Women in Machine Learning (WiML), and co-organized the past three NIPS Workshop on Machine Learning for Health (ML4H). She serves as a NeurIPS 2019 Workshop Co-Chair, and Board Member of the Machine Learning for Health Unconference. Previously, she was a Visiting Researcher with Alphabet's Verily and a post-doc with Dr. Peter Szolovits at MIT (CV). Marzyeh targets “Healthy ML”, focusing on creating applying machine learning to understand and improve health.

Professor Ghassemi completed her PhD at MIT where her research focused on machine learning in health care. Prior to MIT, she received a Master’s degree in biomedical engineering from Oxford University as a Marshall Scholar and B.S. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University.

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Event Sponsor: 
Graduate School of Business, Institute for Computational and Mathematical Engineering (ICME), Stanford Center on Philanthropy and Civil Society, School of Engineering, Stanford Institute for Human-Centered Artificial Intelligence (HAI)
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