Rajen Subba

33 total papers · 781 total citations
18 papers, 390 citations indexed

About

Rajen Subba is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Rajen Subba has authored 18 papers receiving a total of 390 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 2 papers in Information Systems and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Rajen Subba's work include Topic Modeling (15 papers), Natural Language Processing Techniques (15 papers) and Speech and dialogue systems (7 papers). Rajen Subba is often cited by papers focused on Topic Modeling (15 papers), Natural Language Processing Techniques (15 papers) and Speech and dialogue systems (7 papers). Rajen Subba collaborates with scholars based in United States, Israel and Australia. Rajen Subba's co-authors include Seungwhan Moon, Pararth Shah, Barbara Di Eugenio, Anuj Kumar, Jinfeng Rao, Anusha Balakrishnan, Michael White, Nicholas Green, Paul Crook and Zhiguang Wang and has published in prestigious journals such as Language Resources and Evaluation and North American Chapter of the Association for Computational Linguistics.

In The Last Decade

Rajen Subba

18 papers receiving 361 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Rajen Subba 362 83 58 18 9 18 390
Claudiu Musat 300 0.8× 78 0.9× 52 0.9× 22 1.2× 4 0.4× 20 338
Yuhai Yu 272 0.8× 110 1.3× 57 1.0× 7 0.4× 16 1.8× 14 403
Giuseppe Castellucci 287 0.8× 63 0.8× 57 1.0× 9 0.5× 10 1.1× 22 341
Vlad Niculae 288 0.8× 72 0.9× 39 0.7× 15 0.8× 5 0.6× 28 344
Zeyang Lei 313 0.9× 58 0.7× 41 0.7× 7 0.4× 9 1.0× 16 362
Xinxiong Chen 411 1.1× 40 0.5× 86 1.5× 22 1.2× 9 1.0× 10 443
Xiangju Li 276 0.8× 39 0.5× 51 0.9× 52 2.9× 12 1.3× 20 339
Daniel Beck 304 0.8× 62 0.7× 45 0.8× 15 0.8× 14 1.6× 29 363
Hongkui Yu 354 1.0× 44 0.5× 104 1.8× 27 1.5× 11 1.2× 9 428
Shuai Wang 350 1.0× 39 0.5× 67 1.2× 7 0.4× 10 1.1× 30 433

Countries citing papers authored by Rajen Subba

Since Specialization
Citations

This map shows the geographic impact of Rajen Subba'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 Rajen Subba with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rajen Subba more than expected).

Fields of papers citing papers by Rajen Subba

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rajen Subba. 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 Rajen Subba. The network helps show where Rajen Subba may publish in the future.

Co-authorship network of co-authors of Rajen Subba

This figure shows the co-authorship network connecting the top 25 collaborators of Rajen Subba. A scholar is included among the top collaborators of Rajen Subba 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 Rajen Subba. Rajen Subba is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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.

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