Vishakh Padmakumar
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- General Social Sciences top 5%
- Communication
- Sociology and Political Science
- Co-authors
- He HeNikita NangiaJason PhangJana ThompsonAlicia ParrishSamuel BowmanTuhin ChakrabartyPhu Mon Htut
- Topics
- Topic Modeling (9 papers)Natural Language Processing Techniques (7 papers)Multimodal Machine Learning Applications (3 papers)
- Journals
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language TechnologiesFindings of the Association for Computational Linguistics: ACL 2022Creativity and Cognition
- Partner nations
- United StatesIndiaUnited Kingdom
In The Last Decade
Vishakh Padmakumar
10 papers receiving 170 citations
Peers
Comparison fields: 5 of 43
- Artificial Intelligence 134
- Computer Vision and Pattern Recognition 24
- General Social Sciences 18
- Communication 14
- Sociology and Political Science 14
Countries citing papers authored by Vishakh Padmakumar
This map shows the geographic impact of Vishakh Padmakumar'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 Vishakh Padmakumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vishakh Padmakumar more than expected).
Fields of papers citing papers by Vishakh Padmakumar
This network shows the impact of papers produced by Vishakh Padmakumar. 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 Vishakh Padmakumar. The network helps show where Vishakh Padmakumar may publish in the future.
Co-authorship network of co-authors of Vishakh Padmakumar
This figure shows the co-authorship network connecting the top 25 collaborators of Vishakh Padmakumar. A scholar is included among the top collaborators of Vishakh Padmakumar 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 Vishakh Padmakumar. Vishakh Padmakumar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 11 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 64 | |
| 6 | 21 | |
| 7 | 18 | |
| 8 | 5 | |
| 9 | 14 | |
| 10 | 4 | |
| 11 | 18 | |
| 12 | 1 | |
| 13 | 23 |
About Vishakh Padmakumar
Vishakh Padmakumar is a scholar working on General Social Sciences, Artificial Intelligence and Ophthalmology, having authored 13 papers that have together received 180 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (7 papers) and Multimodal Machine Learning Applications (3 papers). The work is most often cited by research in Health Informatics (11 citations), General Social Sciences (18 citations) and Artificial Intelligence (134 citations). Vishakh Padmakumar has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include He He, Nikita Nangia, Jason Phang, Jana Thompson, Alicia Parrish, Samuel Bowman, Tuhin Chakrabarty, Phu Mon Htut, Jonathan Nagler and Fridolin Linder. Their work appears in journals such as Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Findings of the Association for Computational Linguistics: ACL 2022 and Creativity and Cognition.
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.