Marzyeh Ghassemi’s research goal is to create novel machine learning approaches that can be used to improve healthcare delivery, understand what it means to be healthy, and quantify the impact of possible interventions.
Many of the most interesting technical questions in machine learning are inspired by real use cases, and the explosion of clinical data provides an exciting new set of challenges. Ghassemi’s work explores the frontiers of causality, time series analysis and representation learning in this effort. The overall goal of her group is to learn “healthy” models of human health.
- 2008 British Marshall Scholar
- 2018 MIT TechReview “35 Innovators Under 35”
- 2018 Canada CIFAR AI Chair
- 2019 Canada Research Chair in Machine Learning for Health, Natural Sciences and Engineering Research Council (NSERC)
- Clinical Intervention Prediction and Understanding with Deep Neural Networks H Suresh, N Hunt, A Johnson, LA Celi, P Szolovits, M Ghassemi Machine Learning for Healthcare Conference, 322-337
- Predicting intervention onset in the ICU with switching state space models M Ghassemi, M Wu, MC Hughes, P Szolovits, F Doshi-Velez AMIA Summits on Translational Science Proceedings 2017, 82
- Can AI Help Reduce Disparities in General Medical and Mental Health Care? IY Chen, P Szolovits, M Ghassemi AMA Journal of Ethics 21 (2), 167-179
- Practical guidance on artificial intelligence for health-care data M Ghassemi, T Naumann, P Schulam, AL Beam, IY Chen, R Ranganath The Lancet Digital Health 1 (4), e157-e159
- Semi-Supervised Biomedical Translation with Cycle Wasserstein Regression GANs MBA McDermott, T Yan, T Naumann, N Hunt, H Suresh, P Szolovits, ... Thirty-Second AAAI Conference on Artificial Intelligence
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta, Ontario, and Quebec as well as foundations, individuals, corporations, and international partner organizations.