Shuo Yang
- Molecular Biology
- Plant Science
- Computer Vision and Pattern Recognition top 10%
- Industrial and Manufacturing Engineering top 10%
- Atomic and Molecular Physics, and Optics
- Co-authors
- Qiang CuiYibin LiDemian RiccardiWei ZhangRan SongLuqi HuangJiyu ChengPuja Goyal
- Topics
- Robot Manipulation and Learning (9 papers)Energy, Environment, Economic Growth (6 papers)Environmental Impact and Sustainability (6 papers)
- Cited by
- Industrial and Manufacturing EngineeringComputer Vision and Pattern RecognitionPlant Science
- Journals
- Proceedings of the National Academy of SciencesPLoS ONEInternational Journal of Molecular Sciences
- Partner nations
- ChinaUnited StatesJapan
In The Last Decade
Shuo Yang
51 papers receiving 616 citations
Peers
Comparison fields: 5 of 123
- Molecular Biology 230
- Plant Science 129
- Computer Vision and Pattern Recognition 80
- Industrial and Manufacturing Engineering 58
- Atomic and Molecular Physics, and Optics 55
Countries citing papers authored by Shuo Yang
This map shows the geographic impact of Shuo Yang'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 Shuo Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shuo Yang more than expected).
Fields of papers citing papers by Shuo Yang
This network shows the impact of papers produced by Shuo Yang. 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 Shuo Yang. The network helps show where Shuo Yang may publish in the future.
Co-authorship network of co-authors of Shuo Yang
This figure shows the co-authorship network connecting the top 25 collaborators of Shuo Yang. A scholar is included among the top collaborators of Shuo Yang 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 Shuo Yang. Shuo Yang 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 | 0 | |
| 4 | 7 | |
| 5 | 9 | |
| 6 | 19 | |
| 7 | 1 | |
| 8 | 7 | |
| 9 | 11 | |
| 10 | 4 | |
| 11 | 5 | |
| 12 | 0 | |
| 13 | 2 | |
| 14 | 1 | |
| 15 | 1 | |
| 16 | 2 | |
| 17 | 10 | |
| 18 | 24 | |
| 19 | 52 | |
| 20 | 32 |
About Shuo Yang
Shuo Yang is a scholar working on Computer Vision and Pattern Recognition, Transportation and Biophysics, having authored 55 papers that have together received 625 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (9 papers), Energy, Environment, Economic Growth (6 papers) and Environmental Impact and Sustainability (6 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (58 citations), Computer Vision and Pattern Recognition (80 citations) and Plant Science (129 citations). Shuo Yang has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Qiang Cui, Yibin Li, Demian Riccardi, Wei Zhang, Ran Song, Luqi Huang, Wei Zhang, Jiyu Cheng, Puja Goyal and M. R. Gunner. Their work appears in journals such as Proceedings of the National Academy of Sciences, PLoS ONE and International Journal of Molecular Sciences.
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.