Rahul Mazumder

3.2k total citations · 1 hit paper
36 papers, 1.3k citations indexed

About

Rahul Mazumder is a scholar working on Computational Mechanics, Artificial Intelligence and Statistics and Probability. According to data from OpenAlex, Rahul Mazumder has authored 36 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computational Mechanics, 14 papers in Artificial Intelligence and 11 papers in Statistics and Probability. Recurrent topics in Rahul Mazumder's work include Sparse and Compressive Sensing Techniques (12 papers), Statistical Methods and Inference (10 papers) and Advanced Optimization Algorithms Research (5 papers). Rahul Mazumder is often cited by papers focused on Sparse and Compressive Sensing Techniques (12 papers), Statistical Methods and Inference (10 papers) and Advanced Optimization Algorithms Research (5 papers). Rahul Mazumder collaborates with scholars based in United States, India and Australia. Rahul Mazumder's co-authors include Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, Jason D. Lee, Dimitris Bertsimas, Haihao Lu, Dennis L. Sun, Bodhisattva Sen, Garud Iyengar and B. S. Mazumder and has published in prestigious journals such as Nature Communications, Journal of the American Statistical Association and Biometrics.

In The Last Decade

Rahul Mazumder

33 papers receiving 1.2k citations

Hit Papers

Spectral Regularization Algorithms for Learning Large Inc... 2010 2026 2015 2020 2010 100 200 300 400 500
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Spectral Regularization Algorithms for Learning Large Incomplete Matrices.
    2010 592 citations Rahul Mazumder, Trevor Hastie et al. PubMed profile →

Peers

Rahul Mazumder
Holger Höfling United States
Yiyuan She United States
Guillaume Obozinski Switzerland
Cheolwoo Park South Korea
Ery Arias-Castro United States
Taiji Suzuki Japan
Debashis Paul United States
Haipeng Shen United States
Holger Höfling United States
Rahul Mazumder
Citations per year, relative to Rahul Mazumder Rahul Mazumder (= 1×) peers Holger Höfling

Countries citing papers authored by Rahul Mazumder

Since Specialization
Citations

This map shows the geographic impact of Rahul Mazumder's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rahul Mazumder with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rahul Mazumder more than expected).

Fields of papers citing papers by Rahul Mazumder

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rahul Mazumder. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rahul Mazumder. The network helps show where Rahul Mazumder may publish in the future.

Co-authorship network of co-authors of Rahul Mazumder

This figure shows the co-authorship network connecting the top 25 collaborators of Rahul Mazumder. A scholar is included among the top collaborators of Rahul Mazumder based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Rahul Mazumder. Rahul Mazumder is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Mazumder, Rahul, et al.. (2024). Optimal ensemble construction for multistudy prediction with applications to mortality estimation. Statistics in Medicine. 43(9). 1774–1789. 1 indexed citations
2.
Li, Hui, Rahul Mazumder, & Xihong Lin. (2023). Accurate and efficient estimation of local heritability using summary statistics and the linkage disequilibrium matrix. Nature Communications. 14(1). 7954–7954. 7 indexed citations
3.
Mazumder, Rahul, et al.. (2023). Fire: An Optimization Approach for Fast Interpretable Rule Extraction. arXiv (Cornell University). 1396–1405. 3 indexed citations
4.
Wang, Haoyue, Haihao Lu, & Rahul Mazumder. (2022). Frank--Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning. SIAM Journal on Optimization. 32(4). 2938–2968. 3 indexed citations
5.
Mazumder, Rahul, et al.. (2022). Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 666–675. 1 indexed citations
6.
Dey, Santanu S., et al.. (2021). Using ℓ1-Relaxation and Integer Programming to Obtain Dual Bounds for Sparse PCA. Operations Research. 70(3). 1914–1932. 8 indexed citations
7.
Moult, Eric M., Yingying Shi, Qinqin Zhang, et al.. (2021). Analysis of correlations between local geographic atrophy growth rates and local OCT angiography-measured choriocapillaris flow deficits. Biomedical Optics Express. 12(7). 4573–4573. 17 indexed citations
8.
Ghoting, Amol, et al.. (2020). ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications. DSpace@MIT (Massachusetts Institute of Technology). 1. 704–714. 4 indexed citations
9.
Mazumder, Rahul, et al.. (2020). Matrix completion with nonconvex regularization: spectral operators and scalable algorithms. Statistics and Computing. 30(4). 1113–1138. 3 indexed citations
10.
Bertsimas, Dimitris, et al.. (2019). Learning a Mixture of Gaussians via Mixed-Integer Optimization. 1(3). 221–240. 3 indexed citations
11.
Zhu, Zhen, et al.. (2018). Hierarchical Modeling and Shrinkage for User Session LengthPrediction in Media Streaming. DSpace@MIT (Massachusetts Institute of Technology). 2 indexed citations
12.
Fithian, William & Rahul Mazumder. (2018). Flexible Low-Rank Statistical Modeling with Missing Data and Side Information. Statistical Science. 33(2). 11 indexed citations
13.
Mazumder, Rahul, et al.. (2018). A Computational Framework for Multivariate Convex Regression and Its Variants. Journal of the American Statistical Association. 114(525). 318–331. 28 indexed citations
14.
Freund, Robert M., et al.. (2017). A new perspective on boosting in linear regression via subgradient optimization and relatives. Project Euclid (Cornell University). 16 indexed citations
15.
Mazumder, Rahul & Trevor Hastie. (2012). The graphical lasso: New insights and alternatives. Electronic Journal of Statistics. 6(none). 2125–2149. 1 indexed citations
16.
Mazumder, Rahul & Trevor Hastie. (2012). Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso.. PubMed. 13. 781–794. 53 indexed citations
17.
Mazumder, Rahul, Jerome H. Friedman, & Trevor Hastie. (2011). SparseNet: Coordinate Descent With Nonconvex Penalties. Journal of the American Statistical Association. 106(495). 1125–1138. 291 indexed citations
18.
Mazumder, Rahul. (2008). Fluid flow pattern analysis in a trough region: a nonparametric approach. Journal of Applied Statistics. 35(6). 633–645. 1 indexed citations
19.
Mazumder, Rahul. (2007). Clustering based on geometry and interactions of turbulence bursting rate processes in a trough region. Environmetrics. 18(4). 445–459. 4 indexed citations
20.
Mazumder, Rahul. (1992). An Efficient Design of Embedded Memories and their Testability Analysis using Markov Chains. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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