Jun Gao

3.2k citations
90 papers · 1.3k · h-index 18

Impact in

Papers in

Jun Gao

85 papers receiving 1.2k citations

Peers

Jun Gao
Comparison fields: 5 of 143
  • Artificial Intelligence 397
  • Control and Systems Engineering 179
  • Computer Vision and Pattern Recognition 147
  • Statistical and Nonlinear Physics 80
  • Signal Processing 62
Replace Alireza Bagheri with:
Alireza Bagheri Iran
Vijay Nath India
Zhaoxia Wang China
Chen Zhao China
Davide Bacciu Italy
Chao Yu China
Feyzullah Temurtaş Türkiye
James M. Davenport United States
C. Kambhampati United Kingdom
Muhammad Shahzad Younis Pakistan
Jun Gao relative to Alireza Bagheri Iran Alireza Bagheri's profile →
Citations per field
00.5×8.5×
Alireza Bagheri · 1×
Citations per year

Countries citing papers authored by Jun Gao

Since Specialization
Citations

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

Fields of papers citing papers by Jun Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun Gao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun Gao Line = papers co-authored together Jun Gao links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 90 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020163
2 2017130
3 200969
4 200662
5 200956
6 201056
7 202148
8 201847
9 201846
10 200842
11 201237
12 202132
13 201927
14 201725
15 202225
16 202124
17 201323
18 202219
19 201117
20 201216

About Jun Gao

Jun Gao is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Mechanical Engineering and Biomedical Engineering, having authored 90 papers that have together received 1.3k indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Domain Adaptation and Few-Shot Learning (5 papers), Wireless Signal Modulation Classification (5 papers), Image Processing Techniques and Applications (4 papers), Palliative Care and End-of-Life Issues (4 papers), Natural Language Processing Techniques (4 papers), Advanced Neural Network Applications (3 papers) and Fault Detection and Control Systems (3 papers). The work is most often cited by research in Artificial Intelligence (397 citations), Control and Systems Engineering (179 citations), Computer Vision and Pattern Recognition (147 citations), Statistical and Nonlinear Physics (80 citations) and Signal Processing (62 citations). Jun Gao has collaborated with scholars based in China, Canada and United States. Frequent co-authors include Dan Cao, Yu Zhou, Xianzhen Xu, Conrad V. Fernandez, Charles Weijer, Xiaofei Liu, Chang Zhou, Yuqiong Liu, Gaoming Huang and Eric Kodish. Their work appears in journals such as IET Radar Sonar & Navigation, Mechanical Systems and Signal Processing, Assembly Automation, British Journal of Haematology and Pattern Recognition Letters.

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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