Assistant Professor, Department of Electrical Engineering and Computer Science, and Institute for Medical Engineering & Science
Marzyeh Ghassemi is an assistant professor with appointments in the Department of Electrical Engineering and Computer Science, and the Institute for Medical Engineering & Science at MIT. Ghassemi’s research interests span representation learning, behavioral ML, healthcare ML, and healthy ML. One of her focuses is on real-world applications of machine learning, such as turning diverse clinical data into cohesive information with the ability to predict patient needs. Ghassemi has received BS degrees in computer science and electrical engineering from New Mexico State University, an MSc degree in biomedical engineering from Oxford University, and PhD in computer science from MIT.
- Roth, K., Milbich, T., Ommer, B., Cohen, J. P., Ghassemi, M. (2021). Simultaneous Similarity-based Self-Distillation for Deep Metric Learning. International Conference on Machine Learning, 9095-9106.
- McDermott, M., Nestor, B., Kim, E., Zhang, W., Goldenberg, A., Szolovits, P., Ghassemi, M. (2021). A comprehensive EHR timeseries pre-training benchmark. Proceedings of the Conference on Health, Inference, and Learning, 257-278.
- Zhang, H., Dullerud, N., Seyyed-Kalantari, L., Morris, Q., Joshi, S., Ghassemi, M. (2021). An empirical framework for domain generalization in clinical settings. Proceedings of the Conference on Health, Inference, and Learning, 279-290.
- Gaube, S., Suresh, H., Raue, M. et al. Do as AI say: susceptibility in deployment of clinical decision-aids. npj Digit. Med. 4, 31 (2021).
- Colak, E., Moreland, R., Ghassemi, M. (2021). Five principles for the intelligent use of AI in medical imaging. Intensive Care Medicine, 1-3.
- Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi. 2021. Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings. In Conference on Fairness, Accountability, and Transparency (FAccT ’21), March 3–10, 2021, Virtual Event, Canada. ACM, New York, NY, USA, 33 pages. https://doi.org/10.1145/3442188.3445934
- Beam AL, Manrai AK, Ghassemi M. Challenges to the Reproducibility of Machine Learning Models in Health Care. JAMA. 2020;323(4):305–306. doi:10.1001/jama.2019.20866
- Cohen, J. P., Morrison, P., Dao, L., Roth, K., Duong, T. Q., Ghassemi, M. (2020). COVID-19 Image Data Collection: Prospective Predictions Are the Future. Journal of Machine Learning for Biomedical Imaging. 2020:2. pp 1-38.
- June 1, 2021: MIT News, The potential of artificial intelligence to bring equity in health care.
- October 2, 2020: MIT Technology Review, How an AI tool for fighting hospital deaths actually worked in the real world.
- August 23, 2019: IEEE Spectrum, 3 Easy Ways to Evaluate AI Claims.
- August 21, 2017: MIT News, Using machine learning to improve patient care.