Vaishnavh Nagarajan
- Artificial Intelligence
- Computational Mathematics top 10%
- Computer Vision and Pattern Recognition
- Computational Mechanics
- Statistical and Nonlinear Physics
- Co-authors
- J. Zico KolterKijung ShinHemank LambaGarth A. GibsonMilind TambeGregory R. GangerLeandro Soriano MarcolinoPradeep Ravikumar
- Topics
- Advanced Neural Network Applications (2 papers)Domain Adaptation and Few-Shot Learning (2 papers)Bacillus and Francisella bacterial research (1 paper)
- Journals
- USENIX Annual Technical ConferencearXiv (Cornell University)Neural Information Processing Systems
- Partner nations
- United StatesIndiaCanada
In The Last Decade
Vaishnavh Nagarajan
7 papers receiving 41 citations
Peers
Comparison fields: 5 of 27
- Artificial Intelligence 26
- Computational Mathematics 12
- Computer Vision and Pattern Recognition 11
- Computational Mechanics 10
- Statistical and Nonlinear Physics 6
Countries citing papers authored by Vaishnavh Nagarajan
This map shows the geographic impact of Vaishnavh Nagarajan'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 Vaishnavh Nagarajan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vaishnavh Nagarajan more than expected).
Fields of papers citing papers by Vaishnavh Nagarajan
This network shows the impact of papers produced by Vaishnavh Nagarajan. 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 Vaishnavh Nagarajan. The network helps show where Vaishnavh Nagarajan may publish in the future.
Co-authorship network of co-authors of Vaishnavh Nagarajan
This figure shows the co-authorship network connecting the top 25 collaborators of Vaishnavh Nagarajan. A scholar is included among the top collaborators of Vaishnavh Nagarajan 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 Vaishnavh Nagarajan. Vaishnavh Nagarajan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Revisiting Adversarial Risk | 1 |
| 2 | Uniform convergence may be unable to explain generalization in deep learning | 12 |
| 3 | 9 | |
| 4 | Geriatrix: aging what you see and what you don't see a file system aging approach for modern storage systems | 4 |
| 5 | 15 | |
| 6 | 1 | |
| 7 | 2 |
About Vaishnavh Nagarajan
Vaishnavh Nagarajan is a scholar working on Computational Mathematics, Hardware and Architecture and Computer Vision and Pattern Recognition, having authored 7 papers that have together received 44 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Bacillus and Francisella bacterial research (1 paper). The work is most often cited by research in Computational Mathematics (12 citations), Artificial Intelligence (26 citations) and Hardware and Architecture (4 citations). Vaishnavh Nagarajan has collaborated with scholars based in United States, India and Canada. Frequent co-authors include J. Zico Kolter, Kijung Shin, Hemank Lamba, Garth A. Gibson, Milind Tambe, Gregory R. Ganger, Leandro Soriano Marcolino and Pradeep Ravikumar. Their work appears in journals such as USENIX Annual Technical Conference, arXiv (Cornell University) and Neural Information Processing Systems.
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.