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.

Joining the Lab

As an MIT undergrad interested in an UROP: Contact Megan Lewis (mblewis@mit.edu) to determine if there are research slots available for the semester, and schedule a 30 minute session with Dr. Ghassemi.

As an MIT MEng: Contact Megan Lewis (mblewis@mit.edu) with a topic and research plan that is relevant to the group.

As an external student: Apply for the MIT EECS or IMES PhD programs, select Marzyeh Ghassemi as a PI you are interested in working with.


  • Apr 2022 - Congratulations to Taylor, Haoran, and Hyewon, who presented their papers at CHIL 2022!
    • Counterfactually Guided Off-policy Transfer in Clinical Settings [Paper] [Code]
      Taylor W. Killian, Marzyeh Ghassemi, Shalmali Joshi
    • Improving the Fairness of Chest X-ray Classifiers [Paper] [Code]
      Haoran Zhang, Natalie Dullerud, Karsten Roth, Lauren Oakden-Rayner, Stephen Robert Pfohl, Marzyeh Ghassemi
    • Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting [Paper] [Code]
      Kwanhyung Lee*, Hyewon Jeong*, Seyun Kim, Donghwa Yang, Hoon-Chul Kang, Edward Choi
  • Feb 2022 - Congratulations to Natalie on her ICLR 2022 paper! [MIT News Article] [Paper]
  • Jan 2022 - New perspective paper in Patterns on equitable systems in healthcare, receiving coverage in MIT News.
  • Dec 2021 - New paper in Nature Medicine on how deep chest X-ray classifiers underdiagnose different protected groups at different rates.
  • Oct 2021 - Congratulations to Haoran Zhang, Taylor Killian, and Karsten Roth on their accepted NeurIPS 2021 papers!
    • Learning Optimal Predictive Checklists [Paper] [Code]
      Haoran Zhang, Quaid Morris, Berk Ustun*, Marzyeh Ghassemi*
    • Medical Dead-ends and Learning to Identify High-risk States and Treatments [MIT News Article] [Paper] [Code]
      Mehdi Fatemi, Taylor W. Killian, Jayakumar Subramanian, Marzyeh Ghassemi
    • Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning [Paper] [Code]
      Timo Milbich*, Karsten Roth*, Samarth Sinha, Ludwig Schmidt, Marzyeh Ghassemi*, Björn Ommer*