Stefanie Sabrina Jegelka is a German computer scientist whose research in machine learning includes submodular optimization in computer vision[1] and deep learning for graph neural networks.[2] She is an associate professor of computer science at the Massachusetts Institute of Technology,[3] and Alexander von Humboldt Professor at the Technical University of Munich.[4]
As a high school student from a small town in Germany, Jegelka won an award in an annual ThinkQuest competition for the design of educational web sites; her site concerned butterflies.[2][5] She became a bioinformatics student at the University of Tübingen, advised by Ulrike von Luxburg and Michael Kaufmann, with an exchange year at the University of Texas at Austin, and earned a diploma in 2007. Continuing her studies jointly at the Max Planck Institute for Intelligent Systems in Tübingen and at ETH Zurich, she completed a Ph.D. in 2012. Her dissertation, Combinatorial Problems with Submodular Coupling in Machine Learning and Computer Vision, was jointly supervised by Jeff Bilmes, Bernhard Schölkopf, and Andreas Krause.[6][7]
After postdoctoral research from 2012 to 2014 at the University of California, Berkeley with Michael I. Jordan and Trevor Darrell, she became X-Consortium Career Development Assistant Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology in 2015,[6] and was promoted to associate professor with tenure in 2022.[8] She was awarded a Humboldt Professorship in 2022 and joined TU Munich as a Humboldt Professor in 2024.[4][9]
Jegelka received the 2015 German Pattern Recognition Award.[1] She became a Sloan Research Fellow in 2018.[6][9]
She was an invited speaker at the 2022 (virtual) International Congress of Mathematicians.[10]