Stefanie Jegelka

Prof Jegelka is an X-Consortium Career Development Associate Professor at MIT EECS, and a member of CSAIL, IDSS, the Center for Statistics and Machine Learning at MIT. She is also affiliated with the ORC. Before that, she was a postdoc in the AMPlab and computer vision group at UC Berkeley, and a PhD student at the Max Planck Institutes in Tuebingen and at ETH Zurich.
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 is supported by a Sloan Research Fellowship, an NSF CAREER Award, a DARPA Young Faculty Award, an NSF BIGDATA, an Adobe Research award, an STL award and other awards by NSF and DARPA. Previously, she was also supported by a Google Research Award and an MIT RSC award.​

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Data Analysis: Statistical Modeling and Computation in Applications (edX)

May 13th 2024
Data Analysis: Statistical Modeling and Computation in Applications (edX)
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A hands-on introduction to the interplay between statistics and computation for the analysis of real data. -- Part of the MITx MicroMasters program in Statistics and Data Science. Data science requires multi-disciplinary skills ranging from mathematics, statistics, machine learning, problem solving to programming, visualization, and communication skills. In this [...]