Stephanie Jegelka

Stefanie Jegelka is a Humboldt Professor of AI at TU Munich. Before that, she was a tenured professor in the EECS Department at MIT, and a member of the Computer Science and AI Lab (CSAIL) and Institute for Data, Systems and Society (IDSS) at MIT. She did a postdoc at UC Berkeley and obtained her PhD from ETH Zurich and the Max Planck Institute for Intelligent Systems. Her research has spanned topics including learning for discrete and combinatorial objects, and understanding representational and computational structure from perspectives such as geometry, graphs and algorithms. Stefanie has received awards including a Humboldt professorship, a Sloan Research Fellowship, an NSF CAREER Award, a DARPA Young Faculty Award, the German Pattern Recognition Award, a Best Paper Award at ICML, the Frank E. Perkins Award for Excellence in Graduate Advising at MIT, and an invited sectional lecture at the International Congress of Mathematicians. She has co-organized multiple workshops on (discrete) optimization in machine learning, graph representation learning, geometry and weight space learning and other related topics, and served as a program chair of ICML 2022.