Prof. Marzyeh Ghassemi

Associate Professor of Electrical Engineering and Computer Science

Primary DLC

Department of Electrical Engineering and Computer Science

MIT Room: 32-428

Research Summary

Professor Ghassemi led the Machine Learning for Health Group (University of Toronto) targets "Healthy ML", focusing on creating applying machine learning to understand and improve health.

We believe that 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. Health is unlike many success stories in machine learning so far - games like Go and self-driving cars -- because we do not have well-defined goals that can be used to learn rules. The nuance of health also requires that we keep machine learning models "healthy" -- working to ensure that they do not learn biased rules or detrimental recommendations.

Improving health requires targeting and evidence – the group tackles part of this puzzle with machine learning. There are many novel technical opportunities for machine learning in health challenges, and important progress to be made with careful application to domain.

Recent Work

  • Video

    The Pulse of Ethical ML in Health: Marzyeh Ghassemi

    April 1, 2025Conference Video Duration: 16:35
    The Pulse of Ethical ML in Health

    Marzyeh Ghassemi
    Associate Professor, MIT Department of Electrical Engineering and Computer Science (EECS) and Institute for Medical Engineering & Science (IMES)

    Machine learning in health has made impressive progress in recent years, powered by an increasing availability of health-related data and high-capacity models. While many models in health now perform at, or above, humans in a range of tasks across the human lifespan, models also learn societal biases and may replicate or expand them. In this talk, Dr. Marzyeh Ghassemi will focus on the need for machine learning researchers and model developers to create robust models that can be ethically deployed in health settings, and beyond. Dr. Ghassemi's talk will span issues in data collection, outcome definition, algorithm development, and deployment considerations.

    Updating the State of the Art

    June 30, 2022MIT Faculty Feature Duration: 17:28

    Marzyeh Ghassemi
    Assistant Professor, Electrical Engineering and Computer Science