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Make Data Work
Sept 29–Oct 1, 2015 • New York, NY

Submodularity in Machine Learning

Stefanie Jegelka (M.I.T.)
3:30pm–4:00pm Tuesday, 09/29/2015
Hardcore Data Science
Location: 1 E10/1 E11 Level: Advanced
Average rating: *****
(5.00, 2 ratings)

Submodularity and data analytics

Photo of Stefanie Jegelka

Stefanie Jegelka


Stefanie Jegelka is the X-Consortium career development assistant professor at the Department of Electrical Engineering and Computer Science at MIT, and a member of CSAIL and the Institute for Data, Systems and Society. Before joining MIT in Spring 2015, she was a postdoctoral scholar in the AMPLab at UC Berkeley, working with Michael Jordan and Trevor Darrell. She earned her PhD from ETH Zurich in collaboration with the Max Planck Institutes in Tuebingen, Germany, and a Diplom from the University of Tuebingen. She has been a fellow of the German National Academic Foundation, and has received an Anita Borg and several other fellowships, as well as a Best Paper Award at ICML. Her research interests lie in algorithmic machine learning, in particular scalable analytics with combinatorial structure, with applications in various fields including computer vision, biology, and the development of new materials. She has given four tutorials on Submodularity in Machine Learning at international conferences, and has organized several workshops.