Rahul Nair

2.0k total citations
13 papers, 352 citations indexed

About

Rahul Nair is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Critical Care and Intensive Care Medicine. According to data from OpenAlex, Rahul Nair has authored 13 papers receiving a total of 352 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 2 papers in Critical Care and Intensive Care Medicine. Recurrent topics in Rahul Nair's work include Advanced Vision and Imaging (4 papers), Frailty in Older Adults (2 papers) and Explainable Artificial Intelligence (XAI) (2 papers). Rahul Nair is often cited by papers focused on Advanced Vision and Imaging (4 papers), Frailty in Older Adults (2 papers) and Explainable Artificial Intelligence (XAI) (2 papers). Rahul Nair collaborates with scholars based in United States, Germany and India. Rahul Nair's co-authors include Daniel Kondermann, Katrin Honauer, Bernd Jähne, Claus Brenner, Kayur Patel, Kanit Wongsuphasawat, Marc Kirchner, Fred Hohman, Donghao Ren and Dominik Moritz and has published in prestigious journals such as Artificial Intelligence Review, Journal of Palliative Medicine and Journal of Intensive Care Medicine.

In The Last Decade

Rahul Nair

12 papers receiving 344 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rahul Nair United States 8 183 101 37 23 23 13 352
Vitaly Schetinin United Kingdom 14 48 0.3× 235 2.3× 35 0.9× 20 0.9× 38 1.7× 40 461
Saqib Qamar China 10 160 0.9× 119 1.2× 17 0.5× 16 0.7× 86 3.7× 22 382
Ahmad Al Smadi China 9 74 0.4× 132 1.3× 33 0.9× 3 0.1× 76 3.3× 33 393
Lechao Cheng China 10 242 1.3× 139 1.4× 44 1.2× 2 0.1× 44 1.9× 52 377
Mohamed Yaseen Jabarulla South Korea 10 103 0.6× 86 0.9× 30 0.8× 3 0.1× 93 4.0× 18 365
Emine Cengil Türkiye 14 103 0.6× 98 1.0× 16 0.4× 5 0.2× 87 3.8× 29 329
K. Meena India 9 139 0.8× 76 0.8× 23 0.6× 5 0.2× 16 0.7× 43 310
Marcus Bloice Austria 9 78 0.4× 77 0.8× 23 0.6× 4 0.2× 68 3.0× 15 292
Abdul Basit Siddiqui Pakistan 10 44 0.2× 36 0.4× 34 0.9× 5 0.2× 29 1.3× 24 217

Countries citing papers authored by Rahul Nair

Since Specialization
Citations

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

Fields of papers citing papers by Rahul Nair

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rahul Nair

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

All Works

13 of 13 papers shown
1.
2.
Graziani, Mara, Davide Calvaresi, Mor Vered, et al.. (2022). A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences. Artificial Intelligence Review. 56(4). 3473–3504. 65 indexed citations
3.
Hohman, Fred, Dominik Moritz, Kanit Wongsuphasawat, et al.. (2022). Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels. CHI Conference on Human Factors in Computing Systems. 1–13. 47 indexed citations
4.
Daly, Elizabeth, et al.. (2021). User Driven Model Adjustment via Boolean Rule Explanations. Proceedings of the AAAI Conference on Artificial Intelligence. 35(7). 5896–5904. 7 indexed citations
5.
Hope, Aluko A., et al.. (2020). Prehospital Frailty and Screening Criteria for Palliative Care Services in Critically Ill Older Adults: An Observational Cohort Study. Journal of Palliative Medicine. 24(2). 252–256. 3 indexed citations
6.
Nair, Rahul, et al.. (2020). Toxic Comment Detection using LSTM. 1–8. 19 indexed citations
7.
Hope, Aluko A., et al.. (2019). Frailty, Acute Organ Dysfunction, and Increased Disability After Hospitalization in Older Adults Who Survive Critical Illness: A Prospective Cohort Study. Journal of Intensive Care Medicine. 35(12). 1505–1512. 10 indexed citations
8.
Nair, Rahul, et al.. (2018). Towards Automated Extraction of Business Constraints from Unstructured Regulatory Text. International Conference on Computational Linguistics. 157–160. 1 indexed citations
9.
Kondermann, Daniel, et al.. (2016). The HCI Benchmark Suite: Stereo and Flow Ground Truth with Uncertainties for Urban Autonomous Driving. 19–28. 99 indexed citations
10.
Nair, Rahul, Andrew Fitzgibbon, Daniel Kondermann, & Carsten Rother. (2015). Reflection Modeling for Passive Stereo. 2291–2299. 4 indexed citations
11.
Nair, Rahul, et al.. (2013). On-set depth capturing for VFX productions using time of flight. 1–1. 1 indexed citations
12.
Nair, Rahul, et al.. (2013). Ensemble Learning for Confidence Measures in Stereo Vision. 69 indexed citations
13.

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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