Rahul Mazumder

Rahul Mazumder has won the 2020 INFORMS (Institute for Operations Research and the Management Sciences) Optimization Society Prize for Young Researchers.  His winning paper, “Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms” (Operations Research 68(5):1517-1537, September-October 2020), was co-authored with his MIT ORC (Operations Research Center) PhD student Hussein Hazimeh.  The citation notes: “The paper presents a novel algorithm for the Best Subset Selection problem (BSS)…a fundamental problem in high-dimensional statistics, where sparse solutions with good explanatory power are crucial for practical use. Yet, it is NP-hard and…labeled computationally intractable…The authors combine tools and techniques from high-dimensional statistics, continuous optimization, integer programming, and open-source software development to provide the community with a highly effective heuristic for BSS, one which has strong theoretical underpinnings, outperforms existing state-of-the-art codes in many cases, and is easy to generalize to other settings.”

Rahul Mazumder is the recipient of a 2018 Young Investigator Program (YIP) Award from the Office of Naval Research (ONR). Known as one of the oldest and most selective U.S. science and technology basic research programs, the ONR YIP funds early-career academic researchers (investigators) “whose scientific pursuits show outstanding promise for supporting the Department of Defense, while also promoting their professional development.”

The awards were announced last week. Mazumder is one of five MIT awardees among the 31 scientists chosen from a broad spectrum of technical fields, whose research was deemed to hold “strong promise across a wide range of naval-relevant science and technology areas,” “based on past performance, technical merit, potential for scientific breakthrough and long-term university commitment.”

Mazumder’s three-year project, entitled “Combinatorial Statistical Inference with Mathematical Optimization,” will create new methodology to address key inferential problems central to modern applied statistics and data science, using techniques in mathematical optimization. This new methodology could then be applied to a wide range of problems in support of the U.S. Navy and Department of Defense.