University of Toronto
Marzyeh Ghassemi is an Assistant Professor at the University of Toronto in Computer Science and Medicine, affiliated with the Vector Institute. 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, Nature Translational Psychiatry, and Critical Care. She is an active member of the scientific community, and co-organized the past three NIPS Workshop on Machine Learning for Health (ML4H). Professor Ghassemi targets "Healthy ML", focusing on creating applying machine learning to understand and improve health. Past efforts have focused on improved prediction and stratification of relevant human risks with strong clinical collaborations, 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, she 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. 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.
Improving Health Requires Targeting and Evidence
Professor Marzyeh Ghassemi tackles part of this puzzle with machine learning. This talk 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.