Juan Gao
- Cognitive Neuroscience top 5%
- Computer Vision and Pattern Recognition top 10%
- Radiology, Nuclear Medicine and Imaging top 10%
- General Decision Sciences top 5%
- Biomedical Engineering
- Co-authors
- James L. McClellandWilliam T. NewsomePhilip HolmesJonathan D. CohenKonstantinos TsetsosMarius UsherJunqing YuJianzhong Yang
- Topics
- Medical Imaging Techniques and Applications (6 papers)Neural dynamics and brain function (6 papers)Advanced X-ray and CT Imaging (4 papers)
- Partner nations
- ChinaUnited StatesYemen
In The Last Decade
Juan Gao
33 papers receiving 747 citations
Peers
Comparison fields: 5 of 94
- Cognitive Neuroscience 388
- Computer Vision and Pattern Recognition 117
- Radiology, Nuclear Medicine and Imaging 114
- General Decision Sciences 83
- Biomedical Engineering 73
Countries citing papers authored by Juan Gao
This map shows the geographic impact of Juan 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 Juan Gao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Juan Gao more than expected).
Fields of papers citing papers by Juan Gao
This network shows the impact of papers produced by Juan 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 Juan Gao. The network helps show where Juan Gao may publish in the future.
Co-authorship network of co-authors of Juan Gao
This figure shows the co-authorship network connecting the top 25 collaborators of Juan Gao. A scholar is included among the top collaborators of Juan Gao 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 Juan Gao. Juan Gao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 13 | |
| 4 | 7 | |
| 5 | 31 | |
| 6 | 11 | |
| 7 | 14 | |
| 8 | 0 | |
| 9 | 0 | |
| 10 | 21 | |
| 11 | 64 | |
| 12 | 1 | |
| 13 | 84 | |
| 14 | 48 | |
| 15 | 2 | |
| 16 | 141 | |
| 17 | 56 | |
| 18 | Long guide linearity error measurement based on laser interference | 0 |
| 19 | Global asymptotic robust stability of cellular neural networks with delay | 1 |
| 20 | 74 |
About Juan Gao
Juan Gao is a scholar working on General Decision Sciences, Control and Systems Engineering and Cognitive Neuroscience, having authored 39 papers that have together received 762 indexed citations. Recurring topics across this work include Medical Imaging Techniques and Applications (6 papers), Neural dynamics and brain function (6 papers) and Advanced X-ray and CT Imaging (4 papers). The work is most often cited by research in General Decision Sciences (83 citations), Cognitive Neuroscience (388 citations) and Computer Vision and Pattern Recognition (117 citations). Juan Gao has collaborated with scholars based in China, United States and Yemen. Frequent co-authors include James L. McClelland, William T. Newsome, Philip Holmes, Jonathan D. Cohen, Konstantinos Tsetsos, Marius Usher, Junqing Yu, Jianzhong Yang, Tao Guan and Zhanli Hu. Their work appears in journals such as Nucleic Acids Research, PLoS ONE and IEEE Transactions on Automatic Control.
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.