Congratulations to Dr. Stefanie Jegelka for being promoted to associate professor, effective July 1, 2019. Her research spans the theory and practice of algorithmic machine learning and optimization. She is a leader in submodular optimization, a field that has been extremely important for computer vision and machine learning to accommodate problems with discrete combinatorial structures. Her work combines deep theoretical understanding with practical motivation and efficient implementation, providing algorithms with exceptional practical performance and rigorous theoretical guarantees for several key problems.
Dr. Jegelka received her PhD in computer science in 2012 from ETH Zurich and the Max Planck Institute for Intelligent Systems. After serving as a postdoctoral researcher at UC Berkeley, she joined MIT EECS as an assistant professor in early 2015. She has made significant teaching contributions to the department. She developed a graduate course, “Learning with Combinatorial Structure,” that covers models, algorithms, and applications, analyzing how various types of mathematical structures can be used for machine learning. She also co-developed (with Caroline Uhler) a new hands-on data analysis course, “Statistics, Computation, and Applications.” She teaches several other courses, including 6.862 (Applied Machine Learning), 6.437 (Inference and Information), and 6.046 (Design and Analysis of Algorithms). She has received an NSF CAREER Award, a DARPA Young Faculty Award, the German Pattern Recognition Award, and a Sloan Research Fellowship. Stefanie is an affiliate faculty of IDSS.
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