ML Advisory Board
Stefanie Jegelka is the X-Window Consortium Career Development Associate Professor in the Department of EECS at MIT, and a member of the Computer Science and AI Lab (CSAIL) and the Institute for Data, Systems and Society. She is an expert in machine learning, and her research addresses how to make machine learning more flexible and efficient. Her work has made contributions to learning with structured data such as sets and graphs, to the intersection of discrete and continuous optimization, to robust machine learning and learning with limited supervision.
Before joining MIT, Stefanie was a postdoctoral researcher at UC Berkeley, and obtained her PhD from ETH Zurich and the Max Planck Institute for Intelligent Systems. She has received a Sloan Research Fellowship, an NSF CAREER Award, a DARPA Young Faculty Award, the German Pattern Recognition Award and a Best Paper Award at the International Conference on Machine Learning. She has given multiple tutorials at international conferences, organized numerous workshops on topics around discrete and continuous optimization for ML and graph representation learning, and she will serve as a program chair for the International Conference on Machine Learning in 2022.