Shikhar Singh

681 total citations
14 papers, 74 citations indexed

About

Shikhar Singh is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Aerospace Engineering. According to data from OpenAlex, Shikhar Singh has authored 14 papers receiving a total of 74 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 3 papers in Aerospace Engineering. Recurrent topics in Shikhar Singh's work include Spacecraft Design and Technology (3 papers), Multimodal Machine Learning Applications (3 papers) and Topic Modeling (2 papers). Shikhar Singh is often cited by papers focused on Spacecraft Design and Technology (3 papers), Multimodal Machine Learning Applications (3 papers) and Topic Modeling (2 papers). Shikhar Singh collaborates with scholars based in United States, India and Iran. Shikhar Singh's co-authors include Nanyun Peng, Te-Lin Wu, Subhashish Bhattacharya, Rujun Han, Gully Burns, Jiao Sun, Mu Yang, Yu Hou, Pegah Alipoormolabashi and Nuan Wen and has published in prestigious journals such as Journal of Computational and Theoretical Nanoscience, Proceedings of the AAAI Conference on Artificial Intelligence and NCSU Libraries Repository (North Carolina State University Libraries).

In The Last Decade

Shikhar Singh

11 papers receiving 70 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shikhar Singh United States 4 41 22 17 11 11 14 74
Haoze Wu China 6 28 0.7× 32 1.5× 7 0.4× 4 0.4× 3 0.3× 18 60
Daniel W. Stouch United States 5 21 0.5× 21 1.0× 5 0.3× 3 0.3× 12 1.1× 7 52
Filippos Christianos United Kingdom 5 17 0.4× 12 0.5× 11 0.6× 13 1.2× 2 0.2× 9 40
Kumar Shridhar Switzerland 7 75 1.8× 14 0.6× 10 0.6× 2 0.2× 2 0.2× 13 98
Kai Kang China 6 10 0.2× 38 1.7× 8 0.5× 2 0.2× 5 0.5× 16 74
Jiri Hron United Kingdom 5 82 2.0× 28 1.3× 5 0.3× 4 0.4× 2 0.2× 8 104
Marco Fornoni Switzerland 6 34 0.8× 54 2.5× 4 0.2× 2 0.2× 16 1.5× 9 71
Olexa Bilaniuk Canada 5 20 0.5× 40 1.8× 17 1.0× 2 0.2× 13 1.2× 9 64
Léonard Hussenot United States 4 40 1.0× 5 0.2× 5 0.3× 3 0.3× 2 0.2× 5 50
Jack Parker-Holder United Kingdom 4 60 1.5× 3 0.1× 7 0.4× 3 0.3× 2 0.2× 11 78

Countries citing papers authored by Shikhar Singh

Since Specialization
Citations

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

Fields of papers citing papers by Shikhar Singh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shikhar Singh

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

All Works

14 of 14 papers shown
1.
2.
Singh, Shikhar, et al.. (2023). VIPHY: Probing “Visible” Physical Commonsense Knowledge. 7113–7128.
3.
Singh, Shikhar, et al.. (2022). Treatment of proximal tibia fractures with locking compression plate: a prospective study. International Journal of Research in Orthopaedics. 9(1). 47–47.
4.
Singh, Shikhar, James Hegarty, Hugh Leather, & Benoit Steiner. (2022). A graph neural network-based performance model for deep learning applications. 11–20. 3 indexed citations
5.
Sun, Jiao, Mu Yang, Nuan Wen, et al.. (2021). EventPlus: A Temporal Event Understanding Pipeline. 56–65. 18 indexed citations
6.
Singh, Shikhar, Yu Hou, Pegah Alipoormolabashi, et al.. (2021). COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences. 17 indexed citations
7.
Singh, Shikhar, et al.. (2021). Deep Learning Model for Image-Based Plant Diseases Detection on Edge Devices. 1–5. 3 indexed citations
8.
Wu, Te-Lin, et al.. (2021). MELINDA: A Multimodal Dataset for Biomedical Experiment Method Classification. Proceedings of the AAAI Conference on Artificial Intelligence. 35(16). 14076–14084. 12 indexed citations
9.
Singh, Shikhar, et al.. (2020). Adaptive Visual Learning Using Augmented Reality and Machine Learning Techniques. Journal of Computational and Theoretical Nanoscience. 17(11). 4952–4956. 1 indexed citations
10.
Singh, Shikhar, et al.. (2017). A Collaborative Filtering based model for recommending graduate schools. 1–5. 3 indexed citations
11.
12.
Singh, Shikhar, et al.. (2015). GaN FET based CubeSat Electrical Power System. 1388–1395. 10 indexed citations
13.
Singh, Shikhar. (2014). Development of Effective and Efficient Digital Control Architectures for a Scalable and Flexible Electrical Power System of Cube-Satellites and Small Satellites.. NCSU Libraries Repository (North Carolina State University Libraries). 2 indexed citations
14.
Hazra, Samir, et al.. (2013). Device characterization and performance of 1200V/45A SiC JFET module. 273–278. 2 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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