Stefanie Jegelka is a Humboldt Professor at TU Munich and an Associate Professor in the Department of EECS at MIT (on leave). She is a member of the Computer Science and AI Lab (CSAIL), the Center for Statistics and an affiliate of IDSS and ORC. Before joining MIT, she was a postdoctoral researcher at UC Berkeley, and obtained her PhD from ETH Zurich and the Max Planck Institute for Intelligent Systems. Stefanie has received a Sloan Research Fellowship, an NSF CAREER Award, a DARPA Young Faculty Award, an Alexander von Humboldt Professorship, Google research awards, a Two Sigma faculty research award, the German Pattern Recognition Award and a Best Paper Award at the International Conference for Machine Learning (ICML). She has also been invited as a sectional lecturer at the ICM 2022. She has served as an Area Chair for NeurIPS and ICML, as Action Editor for JMLR and as Program Chair for ICML 2022. Her research interests span the theory and practice of algorithmic machine learning, including learning with graphs, learning with symmetries, robustness to distribution shifts, and learning with limited supervision.