Tingxiang Fan
- Computer Vision and Pattern Recognition top 5%
- Aerospace Engineering top 5%
- Artificial Intelligence top 10%
- Control and Systems Engineering top 10%
- Automotive Engineering top 10%
- Co-authors
- Jia PanWenxi LiuPinxin LongTao HanDawei WangDinesh ManochaRuigang YangHua Chen
- Topics
- Robotic Path Planning Algorithms (10 papers)Reinforcement Learning in Robotics (8 papers)Video Surveillance and Tracking Methods (3 papers)
- Journals
- IEEE AccessThe International Journal of Robotics ResearchStructural and Multidisciplinary Optimization
- Partner nations
- Hong KongChinaUnited States
In The Last Decade
Tingxiang Fan
16 papers receiving 559 citations
Peers
Comparison fields: 5 of 62
- Computer Vision and Pattern Recognition 337
- Aerospace Engineering 194
- Artificial Intelligence 188
- Control and Systems Engineering 124
- Automotive Engineering 113
Countries citing papers authored by Tingxiang Fan
This map shows the geographic impact of Tingxiang Fan'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 Tingxiang Fan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tingxiang Fan more than expected).
Fields of papers citing papers by Tingxiang Fan
This network shows the impact of papers produced by Tingxiang Fan. 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 Tingxiang Fan. The network helps show where Tingxiang Fan may publish in the future.
Co-authorship network of co-authors of Tingxiang Fan
This figure shows the co-authorship network connecting the top 25 collaborators of Tingxiang Fan. A scholar is included among the top collaborators of Tingxiang Fan 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 Tingxiang Fan. Tingxiang Fan 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 | 7 | |
| 3 | 25 | |
| 4 | 14 | |
| 5 | 53 | |
| 6 | 40 | |
| 7 | 15 | |
| 8 | 221 | |
| 9 | 104 | |
| 10 | 11 | |
| 11 | 12 | |
| 12 | Learning Resilient Behaviors for Navigation Under Uncertainty Environments. | 1 |
| 13 | 16 | |
| 14 | 39 | |
| 15 | Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation | 2 |
| 16 | 14 |
About Tingxiang Fan
Tingxiang Fan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Automotive Engineering, having authored 16 papers that have together received 576 indexed citations. Recurring topics across this work include Robotic Path Planning Algorithms (10 papers), Reinforcement Learning in Robotics (8 papers) and Video Surveillance and Tracking Methods (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (337 citations), Automotive Engineering (113 citations) and Aerospace Engineering (194 citations). Tingxiang Fan has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include Jia Pan, Wenxi Liu, Pinxin Long, Tao Han, Dawei Wang, Dinesh Manocha, Ruigang Yang, Hua Chen, Xinjing Cheng and Zhiming Chen. Their work appears in journals such as IEEE Access, The International Journal of Robotics Research and Structural and Multidisciplinary Optimization.
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.