Jing-Cheng Shi
- Artificial Intelligence top 10%
- Management Science and Operations Research top 10%
- Computational Theory and Mathematics top 10%
- Information Systems top 10%
- Computer Networks and Communications
- Topics
- Machine Learning and Algorithms (3 papers)Complexity and Algorithms in Graphs (3 papers)Sparse and Compressive Sensing Techniques (2 papers)
- Cited by
- Management Science and Operations ResearchComputational Theory and MathematicsArtificial Intelligence
- Journals
- IEEE Transactions on Evolutionary ComputationIEEE Transactions on MultimediaJournal of Physics Conference Series
In The Last Decade
Jing-Cheng Shi
10 papers receiving 229 citations
Peers
Comparison fields: 5 of 51
- Artificial Intelligence 112
- Management Science and Operations Research 74
- Computational Theory and Mathematics 72
- Information Systems 46
- Computer Networks and Communications 39
Countries citing papers authored by Jing-Cheng Shi
This map shows the geographic impact of Jing-Cheng Shi'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 Jing-Cheng Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jing-Cheng Shi more than expected).
Fields of papers citing papers by Jing-Cheng Shi
This network shows the impact of papers produced by Jing-Cheng Shi. 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 Jing-Cheng Shi. The network helps show where Jing-Cheng Shi may publish in the future.
Co-authorship network of co-authors of Jing-Cheng Shi
This figure shows the co-authorship network connecting the top 25 collaborators of Jing-Cheng Shi. A scholar is included among the top collaborators of Jing-Cheng Shi 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 Jing-Cheng Shi. Jing-Cheng Shi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 4 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | 87 | |
| 6 | Subset Selection under Noise | 22 |
| 7 | 45 | |
| 8 | 5 | |
| 9 | 36 | |
| 10 | Parallel pareto optimization for subset selection | 21 |
About Jing-Cheng Shi
Jing-Cheng Shi is a scholar working on Management Science and Operations Research, Artificial Intelligence and Computational Theory and Mathematics, having authored 10 papers that have together received 232 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (3 papers), Complexity and Algorithms in Graphs (3 papers) and Sparse and Compressive Sensing Techniques (2 papers). The work is most often cited by research in Management Science and Operations Research (74 citations), Computational Theory and Mathematics (72 citations) and Artificial Intelligence (112 citations). Jing-Cheng Shi has collaborated with scholars based in China, Canada and Spain. Frequent co-authors include Chao Qian, Ke Tang, Yang Yu, Zhi‐Hua Zhou, Qing Da, Shiyong Chen, Anxiang Zeng, Yingfei Li, Lijun Zhang and Yinfu Feng. Their work appears in journals such as IEEE Transactions on Evolutionary Computation, IEEE Transactions on Multimedia and Journal of Physics Conference Series.
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.