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个人简介
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, 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 (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.
His work has won several awards at machine learning conferences, 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 (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.
研究兴趣
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NeurIPS (2023)
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SIAM JOURNAL ON OPTIMIZATIONno. 4 (2023): 2885-2908
RESULTS IN MATHEMATICSno. 6 (2023): 1-26
2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDCpp.6166-6171, (2023)
CoRR (2023)
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