Marzyeh Ghassemi 2024 Early Career Fellow
Affiliation Associate Professor, Massachusetts Institute of Technology Hard Problem Solved the challenges and complexities of responsible research, deployment, and sociotechnical embedding of AI into different use-domains, societal spaces, and accounting for different cultures, participants, stakes, risks, societal risks and externalities, and market and other forces.

Dr. Marzyeh Ghassemi is an Associate Professor at MIT in Electrical Engineering and Computer Science (EECS) and Institute for Medical Engineering & Science (IMES). She holds MIT affiliations with the Jameel Clinic, LIDS, IDSS, and CSAIL.

Professor Ghassemi holds a Germeshausen Career Development Professorship, and was named a CIFAR Azrieli Global Scholar and one of MIT Tech Review’s 35 Innovators Under 35. In 2024, she received an NSF CAREER award, and Google Research Scholar Award. Prior to her PhD in Computer Science at MIT, she received an MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar, and B.S. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University.

Professor Ghassemi’s work spans computer science and clinical venues, including NeurIPS, KDD, AAAI, MLHC, JAMIA, JMIR, JMLR, AMIA-CRI, Nature Medicine, Nature Translational Psychiatry, and Critical Care. Her work has been featured in popular press such as MIT News, The Boston Globe, and The Huffington Post.

AI2050 Project

Marzyeh’s project aims to make healthcare AI fairer and more reliable by improving the data used to train these systems. Machine learning models are increasingly used in health settings, but they can be biased against women, minorities, and other underrepresented groups, leading to unfair outcomes. Instead of fixing these biases after training, Marzyeh focuses on cleaning the data beforehand, removing biased or misleading information. Additionally, this project develops tools to monitor and maintain AI performance as medical practices evolve, keeping the AI safe and effective in real-world use. The goal of this work is to ensure models are accurate and equitable.