Jun Rao

24 total papers · 703 total citations
21 papers, 526 citations indexed

About

Jun Rao is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Plant Science. According to data from OpenAlex, Jun Rao has authored 21 papers receiving a total of 526 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computational Theory and Mathematics, 6 papers in Artificial Intelligence and 6 papers in Plant Science. Recurrent topics in Jun Rao's work include Adaptive Dynamic Programming Control (7 papers), Reinforcement Learning in Robotics (6 papers) and Metallurgy and Material Forming (4 papers). Jun Rao is often cited by papers focused on Adaptive Dynamic Programming Control (7 papers), Reinforcement Learning in Robotics (6 papers) and Metallurgy and Material Forming (4 papers). Jun Rao collaborates with scholars based in China, United Kingdom and United States. Jun Rao's co-authors include Dabing Zhang, Sheng Quan, Jingcheng Wang, Jianxin Shi, Jiahui Xu, Chaoyang Hu, Lin Hong, Zhiyong Chen, Hao Feng and Hongyuan Wang and has published in prestigious journals such as Journal of Agricultural and Food Chemistry, Scientific Reports and Journal of the Science of Food and Agriculture.

In The Last Decade

Jun Rao

19 papers receiving 514 citations

Author Peers

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

Author Last Decade Papers Cites
Jun Rao 245 172 100 55 55 21 526
Rui He 65 0.3× 184 1.1× 52 0.5× 19 0.3× 33 0.6× 36 605
Rahul Tripathi 84 0.3× 83 0.5× 149 1.5× 33 0.6× 34 0.6× 48 504
Ahmed M. Alzohairy 212 0.9× 194 1.1× 13 0.1× 44 0.8× 21 0.4× 23 565
Longsheng Chen 150 0.6× 63 0.4× 38 0.4× 49 0.9× 24 0.4× 44 497
Sung G. Lee 40 0.2× 175 1.0× 150 1.5× 13 0.2× 16 0.3× 16 482
Guijuan Wang 204 0.8× 96 0.6× 178 1.8× 15 0.3× 84 1.5× 63 602
R. Sivasamy 75 0.3× 77 0.4× 233 2.3× 29 0.5× 66 1.2× 37 547
Yujie Cao 113 0.5× 172 1.0× 84 0.8× 8 0.1× 161 2.9× 32 619
Hideaki Tanaka 93 0.4× 233 1.4× 66 0.7× 5 0.1× 63 1.1× 39 543
G. Acuña 110 0.4× 117 0.7× 20 0.2× 9 0.2× 9 0.2× 20 473

Countries citing papers authored by Jun Rao

Since Specialization
Citations

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

Fields of papers citing papers by Jun Rao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Rao

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Rao. A scholar is included among the top collaborators of Jun Rao 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 Jun Rao. Jun Rao 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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