Jun Song

573 citations
39 papers · 435 · h-index 14

Impact in

Papers in

Jun Song

37 papers receiving 405 citations

Peers

Jun Song
Comparison fields: 5 of 89
  • Artificial Intelligence 226
  • Atomic and Molecular Physics, and Optics 183
  • Endocrine and Autonomic Systems 37
  • Computer Vision and Pattern Recognition 90
  • Signal Processing 20
Replace Mihai Udrescu with:
Mihai Udrescu Romania
Edward Yellakuor Baagyere China
Xikai Liu China
She-Xiang Jiang China
Hong-Yi Su China
Giuseppe Sergioli Italy
Yaming Yang China
Serge Fehr Netherlands
Jun Song relative to Mihai Udrescu Romania Mihai Udrescu's profile →
Citations per field
00.5×10.2×
Mihai Udrescu · 1×
Citations per year

Countries citing papers authored by Jun Song

Since Specialization
Citations

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

Fields of papers citing papers by Jun Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun Song, 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 Song Line = papers co-authored together Jun Song links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201861
2 201439
3 201533
4 202127
5 200825
6 202123
7 202021
8 201620
9 201117
10 200217
11 201716
12 201616
13 201914
14 200913
15 200811
16 20118
17 20158
18 20107
19 20117
20 20097

About Jun Song

Jun Song is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition, Signal Processing and Applied Mathematics, having authored 39 papers that have together received 435 indexed citations. Recurring topics across this work include Quantum Information and Cryptography (22 papers), Quantum Mechanics and Applications (15 papers), Quantum Computing Algorithms and Architecture (8 papers), Quantum optics and atomic interactions (7 papers), Image and Signal Denoising Methods (5 papers), Multimodal Machine Learning Applications (4 papers), Advanced Image and Video Retrieval Techniques (4 papers) and Mechanical and Optical Resonators (3 papers). The work is most often cited by research in Artificial Intelligence (226 citations), Atomic and Molecular Physics, and Optics (183 citations), Endocrine and Autonomic Systems (37 citations), Computer Vision and Pattern Recognition (90 citations) and Signal Processing (20 citations). Jun Song has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Fei Wu, Hao Yuan, Yueting Zhuang, Kui Hou, Xi Li, Jun Zhou, Zhongfei Zhang, Lian-Fang Han, Hong-Yi Fan and Yanfei Wang. Their work appears in journals such as Chinese Physics Letters, Optics Communications, International Journal of Theoretical Physics, Oncology Reports and IEEE Transactions on Image Processing.

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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