Suvrit Sra, an Associate Professor in MIT’s Department of Electrical Engineering and Computer Science, joined the department, as well as the MIT Laboratory for Information and Decision Systems (LIDS), and the MIT Institute for Data, Systems, and Society (IDSS) as a core faculty member, in January 2018. Prior to this, he was a Principal Research Scientist at LIDS. Before coming to LIDS, he was a Senior Research Scientist at the Max Planck Institute for Intelligent Systems, in Tübingen, Germany. During this time, he was also a visiting faculty member at the University of California at Berkeley (EECS Department) and Carnegie Mellon University (Machine Learning Department). He received his PhD in Computer Science from the University of Texas at Austin.
Suvrit’s research bridges a variety of mathematical topics including optimization, matrix theory, differential geometry, and probability with machine learning. His recent work focuses on the foundations of geometric optimization, an emerging subarea of nonconvex optimization where geometry (often non-Euclidean) enables efficient computation of global optimality. More broadly, his work encompasses a wide range of topics in optimization, especially in machine learning, statistics, signal processing, and related areas. He is pursuing novel applications of machine learning and optimization to materials science, quantum chemistry, synthetic biology, healthcare, and other data-driven domains.
His work has won several awards at machine learning conferences, as well as the 2011 “SIAM Outstanding Paper” award, faculty research awards from Criteo and Amazon, and an NSF CAREER award. In addition, Suvrit founded (and regularly co-chairs) the popular OPT “Optimization for Machine Learning” series of Workshops at the Conference on Neural Information Processing Systems (NeurIPS). He has also edited a well-received book with the same title (MIT Press, 2011).