Chenjia Bai
- Artificial Intelligence top 10%
- Control and Systems Engineering
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Co-authors
- Peng LiuJianye HaoZhen WangHongyao TangZhaopeng MengTianpei YangXianglong TangLingxiao Wang
- Topics
- Reinforcement Learning in Robotics (16 papers)Adaptive Dynamic Programming Control (5 papers)Evolutionary Algorithms and Applications (4 papers)
- Journals
- Journal of the American Statistical AssociationIEEE Transactions on Pattern Analysis and Machine IntelligenceInformation Sciences
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Chenjia Bai
18 papers receiving 167 citations
Peers
Comparison fields: 5 of 49
- Artificial Intelligence 94
- Control and Systems Engineering 41
- Computer Networks and Communications 29
- Computer Vision and Pattern Recognition 27
- Electrical and Electronic Engineering 25
Countries citing papers authored by Chenjia Bai
This map shows the geographic impact of Chenjia Bai'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 Chenjia Bai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chenjia Bai more than expected).
Fields of papers citing papers by Chenjia Bai
This network shows the impact of papers produced by Chenjia Bai. 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 Chenjia Bai. The network helps show where Chenjia Bai may publish in the future.
Co-authorship network of co-authors of Chenjia Bai
This figure shows the co-authorship network connecting the top 25 collaborators of Chenjia Bai. A scholar is included among the top collaborators of Chenjia Bai 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 Chenjia Bai. Chenjia Bai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 5 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 3 | |
| 8 | 0 | |
| 9 | 3 | |
| 10 | 1 | |
| 11 | 0 | |
| 12 | 1 | |
| 13 | 5 | |
| 14 | 81 | |
| 15 | 3 | |
| 16 | 12 | |
| 17 | 11 | |
| 18 | 14 | |
| 19 | 11 | |
| 20 | 13 |
About Chenjia Bai
Chenjia Bai is a scholar working on Artificial Intelligence, Computer Science Applications and Computational Theory and Mathematics, having authored 26 papers that have together received 173 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (16 papers), Adaptive Dynamic Programming Control (5 papers) and Evolutionary Algorithms and Applications (4 papers). The work is most often cited by research in Artificial Intelligence (94 citations), Control and Systems Engineering (41 citations) and Automotive Engineering (16 citations). Chenjia Bai has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Peng Liu, Jianye Hao, Zhen Wang, Hongyao Tang, Zhaopeng Meng, Tianpei Yang, Xianglong Tang, Lingxiao Wang, Zhaoran Wang and Wei Zhao. Their work appears in journals such as Journal of the American Statistical Association, IEEE Transactions on Pattern Analysis and Machine Intelligence and Information 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.