Tingguang Li
- Computer Vision and Pattern Recognition top 5%
- Aerospace Engineering top 10%
- Control and Systems Engineering top 10%
- Artificial Intelligence
- Biomedical Engineering
- Co-authors
- Max Q.‐H. MengDelong ZhuDanny HoChaoqun WangJiankun WangBaopu LiChenming LiKrishnan Srinivasan
- Topics
- Reinforcement Learning in Robotics (8 papers)Robotics and Sensor-Based Localization (7 papers)Robotic Path Planning Algorithms (7 papers)
- Cited by
- Computer Vision and Pattern RecognitionAerospace EngineeringControl and Systems Engineering
- Journals
- IEEE Transactions on Instrumentation and MeasurementIEEE Sensors JournalIEEE Robotics and Automation Letters
- Partner nations
- Hong KongChinaUnited States
In The Last Decade
Tingguang Li
15 papers receiving 289 citations
Peers
Comparison fields: 5 of 41
- Computer Vision and Pattern Recognition 200
- Aerospace Engineering 126
- Control and Systems Engineering 95
- Artificial Intelligence 66
- Biomedical Engineering 47
Countries citing papers authored by Tingguang Li
This map shows the geographic impact of Tingguang Li'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 Tingguang Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tingguang Li more than expected).
Fields of papers citing papers by Tingguang Li
This network shows the impact of papers produced by Tingguang Li. 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 Tingguang Li. The network helps show where Tingguang Li may publish in the future.
Co-authorship network of co-authors of Tingguang Li
This figure shows the co-authorship network connecting the top 25 collaborators of Tingguang Li. A scholar is included among the top collaborators of Tingguang Li 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 Tingguang Li. Tingguang Li 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 | 0 | |
| 3 | 21 | |
| 4 | 1 | |
| 5 | 6 | |
| 6 | 63 | |
| 7 | 6 | |
| 8 | 37 | |
| 9 | 9 | |
| 10 | 12 | |
| 11 | 18 | |
| 12 | 5 | |
| 13 | 22 | |
| 14 | 76 | |
| 15 | 2 | |
| 16 | 9 | |
| 17 | 7 |
About Tingguang Li
Tingguang Li is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering and Aerospace Engineering, having authored 17 papers that have together received 295 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (8 papers), Robotics and Sensor-Based Localization (7 papers) and Robotic Path Planning Algorithms (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (200 citations), Aerospace Engineering (126 citations) and Control and Systems Engineering (95 citations). Tingguang Li has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include Max Q.‐H. Meng, Delong Zhu, Danny Ho, Chaoqun Wang, Jiankun Wang, Baopu Li, Chenming Li, Krishnan Srinivasan, Jeannette Bohg and Max Q.-H. Meng. Their work appears in journals such as IEEE Transactions on Instrumentation and Measurement, IEEE Sensors Journal and IEEE Robotics and Automation Letters.
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.