Stefanie Jegelka is a Visiting Professor at MIT EECS, and a Humboldt Professor at TU Munich. Previously, she was a tenured Associate Professor at MIT, and a member of CSAIL, IDSS, the Center for Statistics and Machine Learning at MIT. She completed her Diplom in bioinformatics at the University of Tübingen in 2007 and her doctorate in informatics at the University of Tübingen and ETH Zurich, Switzerland, in 2012. From 2012 to 2014, she was a postdoc in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, United States.
Her research is in algorithmic machine learning, and spans modeling, optimization algorithms, theory and applications. In particular, she has been working on exploiting mathematical structure for discrete and combinatorial machine learning problems, for robustness and for scaling machine learning algorithms. Her research has been supported by a Sloan Research Fellowship, an NSF CAREER Award, a DARPA Young Faculty Award, an NSF BIGDATA award, an ONR MURI, faculty research awards by Google, Two Sigma and Adobe, an STL award and other awards by NSF and DARPA.