Congratulations to Dr. Suvrit Sra for being promoted to associate professor, effective July 1, 2019. His research bridges areas such as optimization, matrix theory, geometry, and probability with machine learning. He has made fundamental contributions to both convex and nonconvex optimization for machine learning, including developing variance reduction techniques for nonconvex problems with significant improvement over SGD and finding tractable classes of nonconvex optimization problems by exploring the underlying geometry of the problem and discovering the “hidden convexity” in important problems. More broadly, he is interested in data-driven questions within engineering, science, and health care.
Dr. Sra received his PhD in computer science from the University of Texas at Austin in 2007. He was a senior research scientist at the Max Planck Institute for Intelligent Systems and a principal research scientist in LIDS before he joined the EECS faculty in 2018. He is also a core faculty member of IDSS.
Dr. Sra has contributed significantly to the department’s educational mission by teaching both graduate and undergraduate courses in machine learning. He is also active in the larger machine learning community. He founded the OPT (Optimization for Machine Learning) series of workshops at the NIPS conference, which he has co-chaired since 2008; he has also edited a popular book with the same title, published by MIT Press in 2011. His work has won several honors at machine learning venues, including the 2011 SIAM Outstanding Paper Prize.
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