Nagarajan Natarajan

2.7k total citations
63 papers, 1.6k citations indexed

About

Nagarajan Natarajan is a scholar working on Computer Networks and Communications, Artificial Intelligence and Information Systems. According to data from OpenAlex, Nagarajan Natarajan has authored 63 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Computer Networks and Communications, 22 papers in Artificial Intelligence and 11 papers in Information Systems. Recurrent topics in Nagarajan Natarajan's work include Distributed systems and fault tolerance (11 papers), Distributed and Parallel Computing Systems (8 papers) and Machine Learning and Algorithms (7 papers). Nagarajan Natarajan is often cited by papers focused on Distributed systems and fault tolerance (11 papers), Distributed and Parallel Computing Systems (8 papers) and Machine Learning and Algorithms (7 papers). Nagarajan Natarajan collaborates with scholars based in United States, India and Canada. Nagarajan Natarajan's co-authors include Inderjit S. Dhillon, Ambuj Tewari, Pradeep Ravikumar, Kai-Yang Chiang, Cho‐Jui Hsieh, Oluwasanmi Koyejo, Donghyuk Shin, John O. Woods, Edward M. Marcotte and Sriram K. Rajamani and has published in prestigious journals such as Bioinformatics, PLoS ONE and IEEE Communications Magazine.

In The Last Decade

Nagarajan Natarajan

58 papers receiving 1.5k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nagarajan Natarajan United States 19 683 306 298 268 236 63 1.6k
Hisao Tamaki Japan 16 880 1.3× 307 1.0× 96 0.3× 242 0.9× 138 0.6× 62 1.8k
Yeow Meng Chee Singapore 20 776 1.1× 620 2.0× 340 1.1× 375 1.4× 150 0.6× 146 1.8k
Luciana S. Buriol Brazil 20 300 0.4× 733 2.4× 188 0.6× 236 0.9× 191 0.8× 67 1.7k
Shi Han China 24 571 0.8× 389 1.3× 204 0.7× 591 2.2× 48 0.2× 84 1.7k
Jinyin Chen China 21 919 1.3× 295 1.0× 120 0.4× 233 0.9× 332 1.4× 129 1.5k
Márk Jelasity Hungary 23 760 1.1× 1.8k 5.8× 206 0.7× 463 1.7× 260 1.1× 83 2.7k
Sampath Kannan United States 23 961 1.4× 540 1.8× 209 0.7× 223 0.8× 60 0.3× 67 1.8k
S. P. Rajagopalan India 15 405 0.6× 361 1.2× 69 0.2× 473 1.8× 415 1.8× 48 1.3k
Guohui Li China 19 523 0.8× 460 1.5× 67 0.2× 477 1.8× 204 0.9× 187 1.6k
Saeed Jalili Iran 18 510 0.7× 252 0.8× 134 0.4× 335 1.3× 54 0.2× 92 981

Countries citing papers authored by Nagarajan Natarajan

Since Specialization
Citations

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

Fields of papers citing papers by Nagarajan Natarajan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nagarajan Natarajan

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

All Works

20 of 20 papers shown
1.
Natarajan, Nagarajan, et al.. (2024). CORE: Resolving Code Quality Issues using LLMs. Proceedings of the ACM on software engineering.. 1(FSE). 789–811. 11 indexed citations
2.
Bhattacherjee, Debopam, Rohan Gandhi, Venkata N. Padmanabhan, et al.. (2023). Don't Forget the User. 109–116. 5 indexed citations
3.
Natarajan, Nagarajan, et al.. (2023). Simulating Network Paths with Recurrent Buffering Units. Proceedings of the AAAI Conference on Artificial Intelligence. 37(6). 6684–6692. 1 indexed citations
4.
Jain, Naman, et al.. (2022). Jigsaw. 1219–1231. 91 indexed citations
5.
Natarajan, Nagarajan, et al.. (2019). Learning Natural Programs from a Few Examples in Real-Time. International Conference on Artificial Intelligence and Statistics. 1714–1722. 1 indexed citations
6.
Menon, Aditya Krishna, et al.. (2018). Learning from binary labels with instance-dependent noise. Machine Learning. 107(8-10). 1561–1595. 23 indexed citations
7.
Natarajan, Nagarajan, Inderjit S. Dhillon, Pradeep Ravikumar, & Ambuj Tewari. (2017). Cost-sensitive learning with noisy labels. Journal of Machine Learning Research. 18(1). 5666–5698. 20 indexed citations
8.
Dembczyński, Krzysztof, Wojciech Kotłowski, Oluwasanmi Koyejo, & Nagarajan Natarajan. (2017). Consistency Analysis for Binary Classification Revisited. International Conference on Machine Learning. 961–969. 4 indexed citations
9.
Chaudhuri, Kamalika, Prateek Jain, & Nagarajan Natarajan. (2017). Active Heteroscedastic Regression. International Conference on Machine Learning. 694–702. 4 indexed citations
10.
Natarajan, Nagarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, & Inderjit S. Dhillon. (2016). Optimal classification with multivariate losses. International Conference on Machine Learning. 1530–1538. 2 indexed citations
11.
Menon, Aditya Krishna, et al.. (2016). Learning from Binary Labels with Instance-Dependent Corruption. arXiv (Cornell University). 1. 2 indexed citations
12.
Natarajan, Nagarajan & Prateek Jain. (2016). Regret Bounds for Non-decomposable Metrics with Missing Labels. Neural Information Processing Systems. 29. 2874–2882.
13.
Koyejo, Oluwasanmi, Pradeep Ravikumar, Nagarajan Natarajan, & Inderjit S. Dhillon. (2015). Consistent multilabel classification. Neural Information Processing Systems. 28. 3321–3329. 37 indexed citations
14.
Natarajan, Nagarajan & Inderjit S. Dhillon. (2014). Inductive matrix completion for predicting gene–disease associations. Bioinformatics. 30(12). i60–i68. 215 indexed citations
15.
Koyejo, Oluwasanmi, Nagarajan Natarajan, Pradeep Ravikumar, & Inderjit S. Dhillon. (2014). Consistent Binary Classification with Generalized Performance Metrics. Neural Information Processing Systems. 3. 2744–2752. 66 indexed citations
16.
Natarajan, Nagarajan, Inderjit S. Dhillon, Pradeep Ravikumar, & Ambuj Tewari. (2013). Learning with Noisy Labels. Neural Information Processing Systems. 26. 1196–1204. 247 indexed citations
17.
Mı, Chris, et al.. (2004). Field-oriented control of induction motors with direct rotor current estimation. International Power Electronics and Motion Control Conference. 1. 389–392. 1 indexed citations
18.
Barr, William J., et al.. (2003). INA: a distributed processing based approach to information networks. E2/1–E218. 1 indexed citations
19.
Natarajan, Nagarajan & Jian Tang. (1987). Synchronization of Redundant Computation in a Distributed System.. 139–148. 4 indexed citations
20.
Natarajan, Nagarajan, et al.. (1984). A Distributed Deadlock Detection Algorithm Based on Timestamps.. International Conference on Distributed Computing Systems. 546–556. 3 indexed citations

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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