Abhimanu Kumar
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Computer Networks and Communications top 5%
- Information Systems top 5%
- Computational Mathematics top 2%
- Topics
- Stochastic Gradient Optimization Techniques (5 papers)Cloud Computing and Resource Management (4 papers)Topic Modeling (3 papers)
- Journals
- Experimental MechanicsThe International Review of Research in Open and Distributed LearningIEEE Transactions on Big Data
- Partner nations
- United StatesSingaporeCanada
In The Last Decade
Abhimanu Kumar
13 papers receiving 607 citations
Hit Papers
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 394
- Computer Vision and Pattern Recognition 200
- Computer Networks and Communications 171
- Information Systems 134
- Computational Mathematics 69
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-authorship network of co-authors of Abhimanu Kumar
This figure shows the co-authorship network connecting the top 25 collaborators of Abhimanu Kumar. A scholar is included among the top collaborators of Abhimanu Kumar 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 Abhimanu Kumar. Abhimanu Kumar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 53 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | Learning Latent Space Models with Angular Constraints | 9 |
| 5 | Lighter-communication distributed machine learning via Sufficient Factor Broadcasting | 8 |
| 6 | 76 | |
| 7 | 65 | |
| 8 | Petuum: A New Platform for Distributed Machine Learning on Big Databreakdown → | 257 |
| 9 | 30 | |
| 10 | 75 | |
| 11 | 30 | |
| 12 | 9 | |
| 13 | 15 |
About Abhimanu Kumar
Abhimanu Kumar is a scholar working on Computational Mathematics, Artificial Intelligence and Computer Science Applications, having authored 13 papers that have together received 631 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (5 papers), Cloud Computing and Resource Management (4 papers) and Topic Modeling (3 papers). The work is most often cited by research in Computational Mathematics (69 citations), Artificial Intelligence (394 citations) and Computer Vision and Pattern Recognition (200 citations). Abhimanu Kumar has collaborated with scholars based in United States, Singapore and Canada. Frequent co-authors include Eric P. Xing, Qirong Ho, Jinliang Wei, Seunghak Lee, Yaoliang Yu, Pengtao Xie, Xun Zheng, Jin Kyu Kim, Wei Dai and Wei Dai. Their work appears in journals such as Experimental Mechanics, The International Review of Research in Open and Distributed Learning and IEEE Transactions on Big Data.
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.