Abhimanu Kumar
Impact in
- Computational Mathematics top 2%
- Tensor decomposition and applications
- Artificial Intelligence top 5%
- Stochastic Gradient Optimization Techniques
- Privacy-Preserving Technologies in Data
- Data Stream Mining Techniques
Papers in
-
- Stochastic Gradient Optimization Techniques 5
- Topic Modeling 3
- Advanced Graph Neural Networks 3
- Natural Language Processing Techniques 3
- Imbalanced Data Classification Techniques 1
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- Cloud Computing and Resource Management 4
- Co-authors
- Eric P. Xing (8 shared papers)Qirong Ho (6 shared papers)Jinliang Wei (4 shared papers)Seunghak Lee (3 shared papers)Yaoliang Yu (4 shared papers)Pengtao Xie (4 shared papers)Xun Zheng (2 shared papers)Wei Dai (1 shared paper)
- Journals
- The International Review of Research in Open and Distributed Learning (1 paper)Experimental Mechanics (1 paper)IEEE Transactions on Big Data (1 paper)Uncertainty in Artificial Intelligence (1 paper)Research Showcase @ Carnegie Mellon University (Carnegie Mellon University) (1 paper)
- Partner nations
- United StatesSingaporeChina
In The Last Decade
Abhimanu Kumar
13 papers receiving 611 citations
Abhimanu Kumar's Hit Papers
Peers
Comparison fields: 5 of 65
- Computational Mathematics 69
- Artificial Intelligence 392
- Computer Vision and Pattern Recognition 201
- Hardware and Architecture 64
- Computer Science Applications 43
Countries citing papers authored by Abhimanu Kumar
This map shows the geographic impact of Abhimanu Kumar'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 Abhimanu Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Abhimanu Kumar more than expected).
Fields of papers citing papers by Abhimanu Kumar
This network shows the impact of papers produced by Abhimanu Kumar. 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 Abhimanu Kumar. The network helps show where Abhimanu Kumar may publish in the future.
Co-authors
The 25 scholars most cited alongside Abhimanu Kumar, 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 | Petuum: A New Platform for Distributed Machine Learning on Big Data Hit paper breakdown → | 2015 | 257 |
| 2 | 2014 | 77 | |
| 3 | 2015 | 76 | |
| 4 | 2015 | 65 | |
| 5 | 2018 | 53 | |
| 6 | 2014 | 30 | |
| 7 | 2011 | 30 | |
| 8 | 2006 | 15 | |
| 9 | Learning Latent Space Models with Angular Constraints | 2017 | 10 |
| 10 | 2011 | 10 | |
| 11 | Lighter-communication distributed machine learning via Sufficient Factor Broadcasting | 2016 | 8 |
| 12 | 2018 | 3 | |
| 13 | 2018 | 1 |
About Abhimanu Kumar
Abhimanu Kumar is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Computer Science Applications and Computer Networks and Communications, having authored 13 papers that have together received 635 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (5 papers), Cloud Computing and Resource Management (4 papers), Topic Modeling (3 papers), Advanced Graph Neural Networks (3 papers), Natural Language Processing Techniques (3 papers), Graph Theory and Algorithms (2 papers), Imbalanced Data Classification Techniques (1 paper) and Optical measurement and interference techniques (1 paper). The work is most often cited by research in Computational Mathematics (69 citations), Artificial Intelligence (392 citations), Computer Vision and Pattern Recognition (201 citations), Hardware and Architecture (64 citations) and Computer Science Applications (43 citations). Abhimanu Kumar has collaborated with scholars based in United States, Singapore and China. Frequent co-authors include Eric P. Xing, Qirong Ho, Jinliang Wei, Seunghak Lee, Yaoliang Yu, Pengtao Xie, Xun Zheng, Wei Dai, Jin Kyu Kim and Wei Dai. Their work appears in journals such as The International Review of Research in Open and Distributed Learning, Experimental Mechanics, IEEE Transactions on Big Data, Uncertainty in Artificial Intelligence and Research Showcase @ Carnegie Mellon University (Carnegie Mellon University).
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.