Dr. Marzyeh Ghassemi is a distinguished academic leader at the forefront of machine learning applications in healthcare innovation and equity. She currently serves as an Associate Professor at MIT in the Department of Electrical Engineering and Computer Science and the Institute for Medical Engineering & Science, holding prestigious affiliations with the Jameel Clinic LIDS IDSS and CSAIL. Prior to her appointment at MIT in July 2021 she held an Assistant Professorship at the University of Toronto in Computer Science and Medicine where she maintained a Canada CIFAR AI Chair and Canada Research Chair. Her academic foundation includes undergraduate studies in computer science and electrical engineering at New Mexico State University as a Goldwater Scholar a Master of Science in biomedical engineering from Oxford University as a Marshall Scholar and a PhD in Computer Science from MIT.
Dr. Ghassemi leads the Healthy ML laboratory where she develops robust and equitable machine learning algorithms specifically designed to improve healthcare decision-making processes. Her groundbreaking research has uncovered critical issues including algorithmic bias in medical imaging applications notably her influential Nature Medicine publication on underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in underserved patient populations. She has made seminal contributions to understanding the limitations of explainable AI in healthcare through her widely cited Lancet Digital Health paper questioning current approaches to interpretability in medical machine learning systems. Her scholarly work spans both computer science and clinical domains appearing in top-tier venues including NeurIPS KDD Nature Medicine and Critical Care demonstrating rigorous methodology and profound implications for equitable healthcare delivery.
Professor Ghassemi has received numerous prestigious recognitions including being named one of MIT Tech Review's 35 Innovators Under 35 a Sloan Research Fellow and recipient of the 2024 NSF CAREER Award reflecting her significant impact on the field. She holds the Germeshausen Career Development Professorship at MIT and has been recognized with the 2023 MIT Prize for Open Data for her commitment to transparent research practices. In addition to her research leadership she serves as General Chair for the ACM Conference on Health Inference and Learning and has delivered influential talks at major venues including her ICML keynote and Forbes lightning talk. Her teaching encompasses critical courses such as Clinical Data Learning and Ethical Machine Learning in Human Deployments where she prepares the next generation of researchers to address complex challenges at the intersection of artificial intelligence and healthcare with technical rigor and ethical awareness.
