Overview

The “Healthy ML” group at MIT, led by Dr. Marzyeh Ghassemi, focuses on creating and applying machine learning to understand and improve health in ways that are robust, private and fair. Health is important, and improvements in health improve lives. However, we still don’t fundamentally understand what it means to be healthy, and the same patient may receive different treatments across different hospitals or clinicians as new evidence is discovered, or individual illness is interpreted.

Unlike many problems in machine learning - games like Go, self-driving cars, object recognition - disease management does not have well-defined rewards that can be used to learn rules. Models must also be “healthy”, in that they should not learn biased rules or recommendations that harm minorities or minoritized populations. The Healthy ML group tackles the many novel technical opportunities for machine learning in health, and works to make important progress with careful application to this domain.

Read more about our Research Directions and Publications.

News

• September 2024 - Two papers have been published in Nature Computational Science and NEJM AI!
  • Using Labels to Limit AI Misuse in Health [Paper]
    Elaine O. Nsoesie, Marzyeh Ghassemi
  • Settling the Score on Algorithmic Discrimination in Health Care [Paper]
    Marzyeh Ghassemi, Maia Hightower, Elaine O. Nsoesie
• September 2024 - Congratulations to Walter, Haoran, Kimia, Eileen, and Hyewon, whose work has been published in NeurIPS 2024!
  • Test-Time Debiasing of Vision-Language Embeddings
    Walter Gerych, Haoran Zhang, Kimia Hamidieh, Eileen Pan, Maanas K. Sharma, Tom Hartvigsen, Marzyeh Ghassemi
  • Improving Subgroup Robustness via Data Selection [Paper]
    Saachi Jain*, Kimia Hamidieh*, Kristian Georgiev*, Andrew Ilyas, Marzyeh Ghassemi, Aleksander Madry
  • MDAgents: An Adaptive Collaboration of LLMs for Medical Decision Making [Paper] (oral)
    Yubin Kim, Chanwoo Park, Hyewon Jeong, Yik Siu Chan, Xuhai Xu, Daniel McDuff, Hyeonhoon Lee, Marzyeh Ghassemi, Cynthia Breazeal, Hae Won Park
• Aug 2024 - Congratulations to Hyewon and Aparna, whose work has been published in MLHC 2024!
  • Event-Based Contrastive Learning for Medical Time Series [Paper]
    Nassim Oufattole*, Hyewon Jeong*, Matthew Mcdermott, Aparna Balagopalan, Bryan Jangeesingh, Marzyeh Ghassemi, Collin Stultz
• July 2024 - Congratulations to Jiacheng, whose work has been published in ICML 2024!
  • Asymmetry in Low-Rank Adapters of Foundation Models [Paper]
    Jiacheng Zhu, Kristjan Greenewald, Kimia Nadjahi, Haitz Sáez de Ocáriz Borde, Rickard Brüel Gabrielsson, Leshem Choshen, Marzyeh Ghassemi, Mikhail Yurochkin, Justin Solomon
• June 2024 - Congratulations to Haoran, whose work has been published in Nature Medicine and featured in MIT News!
  • The Limits of Fair Medical Imaging AI in Real-World Generalization [Paper]
    Yuzhe Yang*, Haoran Zhang*, Judy W. Gichoya, Dina Katabi, Marzyeh Ghassemi

Joining the Lab

If you are interested in doing an UROP, SUROP, or MEng, please review the lab’s research page, and talk to a graduate student or postdoc who might be closest to your research interest. It is important that all undergraduate students have a graduate or postdoctoral scholar who is close to their research area for advice.

If you are interested in doing a PhD with the Healthy ML group, Dr. Ghassemi admits PhD students from the MIT EECS, IDSS, and HST pools. Please plan to apply to any or all of these programs, and indicate in your application that you would be interested in working with her. Unfortunately, MIT does not allow for direct admission from an individual PI to the institution.

For postdoctoral fellows, please send your resume and a short statement of what work you would plan to do for your time with the lab (with references to relevant content from the lab). Katie O’Reilly (oreilly1@mit.edu) can schedule a 30-minute review meeting once an assessment of a good fit has been made.