Jun Huan
- Artificial Intelligence top 5%
- Computational Theory and Mathematics top 5%
- Molecular Biology
- Computer Vision and Pattern Recognition top 10%
- Computer Networks and Communications top 10%
- Co-authors
- Alexios KoutsoukasXiaoli LiHongliang FeiGerald H. LushingtonChao LanBo LuoJintao ZhangYong Bai
- Topics
- Computational Drug Discovery Methods (19 papers)Bioinformatics and Genomic Networks (8 papers)Face and Expression Recognition (6 papers)
- Cited by
- Computational Theory and MathematicsArtificial IntelligenceComputer Vision and Pattern Recognition
- Partner nations
- United StatesChinaSouth Korea
In The Last Decade
Jun Huan
48 papers receiving 670 citations
Peers
Comparison fields: 5 of 118
- Artificial Intelligence 277
- Computational Theory and Mathematics 166
- Molecular Biology 165
- Computer Vision and Pattern Recognition 127
- Computer Networks and Communications 70
Countries citing papers authored by Jun Huan
This map shows the geographic impact of Jun Huan'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 Huan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Huan more than expected).
Fields of papers citing papers by Jun Huan
This network shows the impact of papers produced by Jun Huan. 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 Huan. The network helps show where Jun Huan may publish in the future.
Co-authorship network of co-authors of Jun Huan
This figure shows the co-authorship network connecting the top 25 collaborators of Jun Huan. A scholar is included among the top collaborators of Jun Huan 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 Huan. Jun Huan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 231 | |
| 3 | 7 | |
| 4 | 1 | |
| 5 | 21 | |
| 6 | 1 | |
| 7 | 20 | |
| 8 | 3 | |
| 9 | 9 | |
| 10 | 27 | |
| 11 | 19 | |
| 12 | 6 | |
| 13 | 4 | |
| 14 | 3 | |
| 15 | 6 | |
| 16 | 22 | |
| 17 | 20 | |
| 18 | Development of Human Poses for the Determination of On-site Construction Productivity in Real-time | 6 |
| 19 | 2 | |
| 20 | 0 |
About Jun Huan
Jun Huan is a scholar working on Computational Mathematics, Computational Theory and Mathematics and Artificial Intelligence, having authored 53 papers that have together received 694 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (19 papers), Bioinformatics and Genomic Networks (8 papers) and Face and Expression Recognition (6 papers). The work is most often cited by research in Computational Theory and Mathematics (166 citations), Artificial Intelligence (277 citations) and Computer Vision and Pattern Recognition (127 citations). Jun Huan has collaborated with scholars based in United States, China and South Korea. Frequent co-authors include Alexios Koutsoukas, Xiaoli Li, Hongliang Fei, Gerald H. Lushington, Chao Lan, Bo Luo, Jintao Zhang, Yong Bai, Min Song and Xiaohong Wang. Their work appears in journals such as IEEE Access, BMC Bioinformatics and Separation and Purification Technology.
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.