Nandan Thakur
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Information Systems
- Signal Processing
- Geography, Planning and Development
- Co-authors
- Kexin WangIryna GurevychNils ReimersJimmy LinEhsan KamallooMehdi RezagholizadehMaik FröbeMartin Potthast
- Topics
- Topic Modeling (8 papers)Natural Language Processing Techniques (7 papers)Data Quality and Management (2 papers)
- Journals
- Transactions of the Association for Computational LinguisticsarXiv (Cornell University)Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Partner nations
- CanadaGermanyUnited States
In The Last Decade
Nandan Thakur
8 papers receiving 57 citations
Peers
Comparison fields: 5 of 15
- Artificial Intelligence 53
- Computer Vision and Pattern Recognition 25
- Information Systems 9
- Signal Processing 5
- Geography, Planning and Development 2
Countries citing papers authored by Nandan Thakur
This map shows the geographic impact of Nandan Thakur'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 Nandan Thakur with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nandan Thakur more than expected).
Fields of papers citing papers by Nandan Thakur
This network shows the impact of papers produced by Nandan Thakur. 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 Nandan Thakur. The network helps show where Nandan Thakur may publish in the future.
Co-authorship network of co-authors of Nandan Thakur
This figure shows the co-authorship network connecting the top 25 collaborators of Nandan Thakur. A scholar is included among the top collaborators of Nandan Thakur 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 Nandan Thakur. Nandan Thakur is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 5 | |
| 7 | 1 | |
| 8 | 7 | |
| 9 | 5 | |
| 10 | 3 | |
| 11 | 34 |
About Nandan Thakur
Nandan Thakur is a scholar working on Artificial Intelligence, Management Science and Operations Research and Signal Processing, having authored 11 papers that have together received 58 indexed citations. Recurring topics across this work include Topic Modeling (8 papers), Natural Language Processing Techniques (7 papers) and Data Quality and Management (2 papers). The work is most often cited by research in Artificial Intelligence (53 citations), Computer Vision and Pattern Recognition (25 citations) and Signal Processing (5 citations). Nandan Thakur has collaborated with scholars based in Canada, Germany and United States. Frequent co-authors include Kexin Wang, Iryna Gurevych, Nils Reimers, Jimmy Lin, Ehsan Kamalloo, Mehdi Rezagholizadeh, Maik Fröbe, Martin Potthast, Matthias Hagen and Qun Liu. Their work appears in journals such as Transactions of the Association for Computational Linguistics, arXiv (Cornell University) and Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
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.