Rahul Mazumder
- Computational Mathematics top 5%
- Statistics and Probability top 1%
- Statistical Methods and Inference 10
- Advanced Statistical Methods and Models 3
- Computational Mechanics top 2%
- Sparse and Compressive Sensing Techniques 12
- Signal Processing top 5%
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- Face and Expression Recognition 3
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- Advanced Optimization Algorithms Research 5
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- Stochastic Gradient Optimization Techniques 4
- Machine Learning and Algorithms 3
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- Wind and Air Flow Studies 3
- Co-authors
- Trevor HastieRobert TibshiraniJerome H. FriedmanJason D. LeeDimitris BertsimasHaihao LuDennis L. SunBodhisattva Sen
- Journals
- Nature Communications (1 paper)Journal of the American Statistical Association (2 papers)Biometrics (1 paper)
- Partner nations
- United StatesIndiaAustralia
In The Last Decade
Rahul Mazumder
33 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 136
- Computational Mathematics 35
- Statistics and Probability 298
- Computational Mechanics 425
- Signal Processing 158
- Computer Vision and Pattern Recognition 283
Countries citing papers authored by Rahul Mazumder
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
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
The 25 scholars most cited alongside Rahul Mazumder, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 1 | |
| 2 | 2023 | 7 | |
| 3 | 2023 | 3 | |
| 4 | 2022 | 3 | |
| 5 | 2022 | 1 | |
| 6 | 2021 | 8 | |
| 7 | 2021 | 17 | |
| 8 | ECLIPSE: An Extreme-Scale Linear Program Solver for Web-Applications | 2020 | 4 |
| 9 | 2020 | 3 | |
| 10 | 2019 | 3 | |
| 11 | Hierarchical Modeling and Shrinkage for User Session LengthPrediction in Media Streaming | 2018 | 2 |
| 12 | 2018 | 11 | |
| 13 | 2018 | 28 | |
| 14 | 2017 | 16 | |
| 15 | 2012 | 1 | |
| 16 | Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso. | 2012 | 53 |
| 17 | 2011 | 291 | |
| 18 | 2008 | 1 | |
| 19 | 2007 | 4 | |
| 20 | An Efficient Design of Embedded Memories and their Testability Analysis using Markov Chains | 1992 | 1 |
About Rahul Mazumder
Rahul Mazumder is a scholar working on Statistics and Probability, Numerical Analysis and Computational Mechanics, having authored 36 papers that have together received 1.3k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (12 papers), Statistical Methods and Inference (10 papers), Advanced Optimization Algorithms Research (5 papers), Stochastic Gradient Optimization Techniques (4 papers), Machine Learning and Algorithms (3 papers), Face and Expression Recognition (3 papers), Wind and Air Flow Studies (3 papers) and Advanced Statistical Methods and Models (3 papers). The work is most often cited by research in Computational Mathematics (35 citations), Statistics and Probability (298 citations) and Computational Mechanics (425 citations). Rahul Mazumder has collaborated with scholars based in United States, India and Australia. Frequent 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. Their work appears in journals such as Nature Communications, Journal of the American Statistical Association and Biometrics.
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.