معرفی
Dr. Marzyeh Ghassemi serves as an Assistant Professor at the University of Toronto with cross-appointments in Computer Science and Medicine. She holds a Canadian CIFAR AI Chair and is a faculty member at the Vector Institute, focusing on bridging artificial intelligence with clinical healthcare applications through rigorous academic research and community leadership.
Her educational background includes dual B.S. degrees in Computer Science and Electrical Engineering from New Mexico State University as a Goldwater Scholar, an MSc in Biomedical Engineering from Oxford University as a Marshall Scholar, and doctoral research at MIT centered on machine learning for clinical risk prediction and patient stratification.
Professor Ghassemi's research program pioneers machine learning solutions for critical healthcare challenges, including physiological state estimation in critical illness, clinical intervention modeling, and phonotraumatic voice disorder diagnosis using wearable sensors. Her methodological expertise spans unsupervised learning, supervised learning, and structured prediction, with significant contributions to translating AI innovations into real-world clinical settings.
Her scientific recognition includes the prestigious Canadian CIFAR AI Chair, Marshall Scholarship, and Goldwater Scholarship, reflecting her exceptional trajectory from undergraduate studies through doctoral research and into independent scholarship.
Professor Ghassemi actively shapes her field through service as NeurIPS 2019 Workshop Co-Chair, Board Member of the Machine Learning for Health Unconference, and organizer of the NIPS Workshop on Machine Learning for Health (2016-2018). She regularly reviews for top venues including NIPS, ICML, KDD, and JMLR while mentoring through formal academic channels.
Her research extends through clinical collaborations with Beth Israel Deaconess Medical Center and Massachusetts General Hospital, fostering interdisciplinary partnerships that connect computational innovation with frontline healthcare delivery and patient outcomes.
