Speaker Profiles

Dr. Marzyeh Ghassemi

Dr. Marzyeh Ghassemi

Assistant Professor, University of Toronto

Faculty Member, Vector Institute

Dr. Marzyeh Ghassemi is an Assistant Professor at the University of Toronto in Computer Science and Medicine, and a Vector Institute faculty member holding a Canadian CIFAR AI Chair. She currently 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.

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, AMIA-CRI, EMBC, Nature Translational Psychiatry, Critical Care, and IEEE Transactions on Biomedical Engineering. She is an active member of the scientific community, reviewing for NIPS, ICML, KDD, MLHC, AAAI, AMIA/AMIA-CRI, and JMLR. She has co-organized the 2016/2017/2018 NIPS Workshop on Machine Learning for Health (ML4H), been 2018 Area Chair for MLHC, and served as an Academic Guest Editor on the 2018 PLoS ONE call on Machine Learning in Health and Biomedicine.

Professor Ghassemi has been invited to speak at a number of academic and industrial institutions. Recently, she was a speaker at The Digital Doctor: Health Care in an Age of AI and Big Data IACS SYMPOSIUM, and a panellist at AMIA 2018 Informatics Summit Panel on Deep Learning for Healthcare - Hype or the Real Thing?, and will appear at Elevate AI 2018, Techna 2018, Syndemics 2018, The Fields Institute, SOCML 2018, and DALI 2019. Professor Ghassemi’s work has been featured online, in popular press such as MIT News, NVIDIA, Huffington Post. She was also recently named one of MIT Tech Review’s 35 Innovators Under 35.

Professor Ghassemi's PhD research at MIT focused on creating and applying machine learning algorithms towards improved prediction and stratification of relevant human risks with clinical collaborations at Beth Israel Deaconess Medical Center and Massachusetts General Hospital, encompassing unsupervised learning, supervised learning, and structured prediction. Her work has been applied to estimating the physiological state of patients during critical illnesses, modeling the need for a clinical intervention, and diagnosing phonotraumatic voice disorders from wearable sensor data. Prior to MIT, Marzyeh received an MSc. 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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