Rahul Mazumder

About

Rahul Mazumder is the Nanyang Technological University Associate Professor of Operations Research and Statistics and an Associate Professor at the MIT Sloan School of Management. His research interests include data science, statistical machine learning, large scale optimization, mathematical programming, and their interplay. He is particularly interested in 'big data' applications in environmental and climate studies, social science, and recommender systems. Mazumder has published in various prestigious journals such as the Journal of Machine Learning Research, Annals of Statistics, Journal of the American Statistical Association, and Annals of Applied Statistics. He completed his BS and MS in statistics from the Indian Statistical Institute, Kolkata in 2007, and earned his PhD in statistics from Stanford University in 2012.

Research topics

• Artificial Intelligence
• Machine Learning
• Computer Science
• Mathematics
• Data Mining
• Engineering
• Algorithm
• Mathematical optimization
• Geometry

Selected publications

• Subset Selection with Shrinkage: Sparse Linear Modeling When the SNR Is Low

  Operations Research · 2022 · 60 citations

  1st authorCorresponding

  Learning Compact High-Dimensional Models in Noisy Environments Building compact, interpretable statistical models where the output depends upon a small number of input features is a well-known problem in modern analytics applications. A fundamental tool used in this context is the prominent best subset selection (BSS) procedure, which seeks to obtain the best linear fit to data subject to a constraint on the number of nonzero features. Whereas the BSS procedure works exceptionally well in some r…

  DOI

• Randomized Gradient Boosting Machine

  SIAM Journal on Optimization · 2020 · 38 citations

  Senior authorCorresponding

  Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 29 October 2018Accepted: 09 June 2020Published online: 07 October 2020Keywordsgradient boosting, ensemble methods, convex optimization, coordinate descent, computational guarantees, first order methodsAMS Subject Headings90C25, 68U01Publication DataISSN (print): 1052-6234ISSN (online): 1095-7189Publisher: Society for Industrial and Applied MathematicsCODEN: sjope8

  DOI

• Integrative multi-omics QTL colocalization maps regulatory architecture in aging human brain

  medRxiv · 2025-04-20 · 7 citations

  preprintOpen access

  Abstract Multi-trait QTL (xQTL) colocalization has shown great promises in identifying causal variants with shared genetic etiology across multiple molecular modalities, contexts, and complex diseases. However, the lack of scalable and efficient methods to integrate large-scale multi-omics data limits deeper insights into xQTL regulation. Here, we propose ColocBoost , a multi-task learning colocalization method that can scale to hundreds of traits, while accounting for multiple causal variants w…

  PublisherOA PDFDOI

• Fast and scalable ensemble learning method for versatile polygenic risk prediction

  Proceedings of the National Academy of Sciences · 2024-08-07 · 4 citations

  articleOpen accessCorresponding

  Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, but existing methods face several limitations, encompassing issues related to computational burden, predictive accuracy, and adaptability to a wide range of genetic architectures. To address these issues, we propose Aggregated L0Learn using Summary-level data (ALL-Sum), a fast and scalable ensemble learning method for computing PRS using summary statistics from genome-wide association studies (G…

  PublisherDOI

• Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems

  ArXiv.org · 2025-02-20 · 1 citations

  preprintOpen accessSenior author

  Large language models (LLMs) have demonstrated remarkable performance across a wide range of industrial applications, from search and recommendation systems to generative tasks. Although scaling laws indicate that larger models generally yield better generalization and performance, their substantial computational requirements often render them impractical for many real-world scenarios at scale. In this paper, we present a comprehensive set of insights for training and deploying small language mo…

  PublisherOA PDFDOI

Recent grants

• III: Small: A New Perspective on Grouped Variable Selection via Modern Optimization

  NSF · $318k · 2017–2022

Frequent coauthors

• Hussein Hazimeh

  26 shared

• Dimitris Bertsimas

  14 shared

• Robert M. Freund

  Massachusetts Institute of Technology

  13 shared

• Paul Grigas

  13 shared

• Kayhan Behdin

  13 shared

• Shibal Ibrahim

  12 shared

• Wenyu Chen

  Chinese University of Hong Kong

  11 shared

• Haoyue Wang

  11 shared

Labs

• MIT Sloan School of ManagementPI

Awards & honors

• 2024 Leo Breiman Junior Award from the Statistical Learning…
• 2023 International Indian Statistical Association (IISA) Ear…
• 2021 Donald P. Gaver, Jr. Early Career Award from INFORMS
• 2018 Young Investigator Program (YIP) Award from the Office…
• 2020 INFORMS Optimization Society Prize for Young Researcher…

Similar researchers at Massachusetts Institute of Technology

• James B. Orlinh-60
• Thomas Magnantih-44