Jun Huan

1.0k citations
53 papers · 697 · h-index 16

Impact in

Papers in

Jun Huan

48 papers receiving 676 citations

Peers

Jun Huan
Comparison fields: 5 of 113
  • Computational Theory and Mathematics 158
  • Artificial Intelligence 277
  • Computer Vision and Pattern Recognition 126
  • Computational Mathematics 3
  • Signal Processing 53
Replace Daniele Grattarola with:
Daniele Grattarola Italy
Razieh Sheikhpour Iran
Changjun Zhou China
Barry Drake United States
Saúl Solorio-Fernández Mexico
Gavin Taylor United States
Kevin Thompson United States
Marcílio C. P. de Souto Brazil
Xiaoye Jiang United States
Jun Huan relative to Daniele Grattarola Italy Daniele Grattarola's profile →
Citations per field
00.5×11.3×
Daniele Grattarola · 1×
Citations per year

Countries citing papers authored by Jun Huan

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 53 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017232
2 201131
3 201227
4 201227
5 200924
6 201023
7 201423
8 201623
9 202223
10 200922
11 201320
12 200820
13 201119
14 201117
15 201117
16 201716
17 201712
18 20129
19 20118
20 20107

About Jun Huan

Jun Huan is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 53 papers that have together received 697 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (19 papers), Bioinformatics and Genomic Networks (7 papers), Face and Expression Recognition (6 papers), Machine Learning and Data Classification (6 papers), Domain Adaptation and Few-Shot Learning (5 papers), Text and Document Classification Technologies (5 papers), Anomaly Detection Techniques and Applications (4 papers) and Sparse and Compressive Sensing Techniques (4 papers). The work is most often cited by research in Computational Theory and Mathematics (158 citations), Artificial Intelligence (277 citations), Computer Vision and Pattern Recognition (126 citations), Computational Mathematics (3 citations) and Signal Processing (53 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, Aaron Smalter, Chao Lan, Jintao Zhang, Bo Luo, Min Song and Yong Bai. Their work appears in journals such as IEEE/ACM Transactions on Computational Biology and Bioinformatics, IEEE Transactions on Knowledge and Data Engineering, BMC Bioinformatics, Journal of Bioinformatics and Computational Biology and Frontiers in Environmental Science.

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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