Nishant Subramani
- Artificial Intelligence
- Signal Processing
- Computer Vision and Pattern Recognition
- Sociology and Political Science
- Health Informatics
- Co-authors
- Alexandra Sasha LuccioniJesse DodgeMatthew E. PetersMargaret MitchellZeerak TalatAaron GokaslanPeter HendersonIsaac Johnson
- Topics
- Topic Modeling (4 papers)Hate Speech and Cyberbullying Detection (2 papers)Natural Language Processing Techniques (2 papers)
- Journals
- arXiv (Cornell University)Proceedings of the AAAI Conference on Artificial IntelligenceFindings of the Association for Computational Linguistics: ACL 2022
- Partner nations
- United StatesDenmarkUnited Kingdom
In The Last Decade
Nishant Subramani
6 papers receiving 62 citations
Peers
Comparison fields: 5 of 25
- Artificial Intelligence 40
- Signal Processing 19
- Computer Vision and Pattern Recognition 16
- Sociology and Political Science 8
- Health Informatics 8
Countries citing papers authored by Nishant Subramani
This map shows the geographic impact of Nishant Subramani'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 Nishant Subramani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nishant Subramani more than expected).
Fields of papers citing papers by Nishant Subramani
This network shows the impact of papers produced by Nishant Subramani. 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 Nishant Subramani. The network helps show where Nishant Subramani may publish in the future.
Co-authorship network of co-authors of Nishant Subramani
This figure shows the co-authorship network connecting the top 25 collaborators of Nishant Subramani. A scholar is included among the top collaborators of Nishant Subramani 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 Nishant Subramani. Nishant Subramani is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 8 | |
| 3 | 21 | |
| 4 | 8 | |
| 5 | 24 | |
| 6 | Can Unconditional Language Models Recover Arbitrary Sentences | 4 |
About Nishant Subramani
Nishant Subramani is a scholar working on Health Informatics, Artificial Intelligence and Safety Research, having authored 6 papers that have together received 67 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Hate Speech and Cyberbullying Detection (2 papers) and Natural Language Processing Techniques (2 papers). The work is most often cited by research in Health Informatics (8 citations), Signal Processing (19 citations) and Artificial Intelligence (40 citations). Nishant Subramani has collaborated with scholars based in United States, Denmark and United Kingdom. Frequent co-authors include Alexandra Sasha Luccioni, Jesse Dodge, Matthew E. Peters, Margaret Mitchell, Zeerak Talat, Aaron Gokaslan, Peter Henderson, Isaac Johnson, Anna Rogers and Rishi Bommasani. Their work appears in journals such as arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence and Findings of the Association for Computational Linguistics: ACL 2022.
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.