Suvrit Sra joined MIT’s Department of Electrical Engineering and Computer Science as an Assistant Professor, and IDSS as a core faculty member, in January 2018. Prior to this, he was a Principal Research Scientist in the MIT Laboratory for Information and Decision Systems (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, the 2011 “SIAM Outstanding Paper” award, and faculty research awards from Criteo and Amazon. 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 (NIPS). He has also edited a well-received book with the same title (MIT Press, 2011).
Suvrit has devoted significant effort to teaching, as well. He has been an invited lecturer on optimization at the Machine Learning Summer School (MLSS) and numerous other specialized short courses. He revamped the Berkeley graduate course, Introduction to Convex Optimization, developed a new advanced course on optimization at CMU, and has regularly co-taught the graduate and undergraduate machine learning courses in EECS at MIT.