Gang Wu
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Electrical and Electronic Engineering
- Information Systems top 5%
- Computer Networks and Communications top 10%
- Co-authors
- Edward Yi ChangKing-Shy GohGuillermo C. BazanYaofeng ChenFengjun HuJohn H. HelsdonRichard D. FarleyQibing Zhu
- Topics
- Data Management and Algorithms (9 papers)Cloud Computing and Resource Management (8 papers)Advanced Database Systems and Queries (8 papers)
- Journals
- Angewandte Chemie International EditionJournal of Geophysical Research AtmospheresEnvironmental Science & Technology
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Gang Wu
95 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 139
- Artificial Intelligence 756
- Computer Vision and Pattern Recognition 592
- Electrical and Electronic Engineering 214
- Information Systems 184
- Computer Networks and Communications 152
Countries citing papers authored by Gang Wu
This map shows the geographic impact of Gang Wu'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 Gang Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gang Wu more than expected).
Fields of papers citing papers by Gang Wu
This network shows the impact of papers produced by Gang Wu. 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 Gang Wu. The network helps show where Gang Wu may publish in the future.
Co-authorship network of co-authors of Gang Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Gang Wu. A scholar is included among the top collaborators of Gang Wu 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 Gang Wu. Gang Wu 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 | 0 | |
| 5 | 7 | |
| 6 | 12 | |
| 7 | 8 | |
| 8 | 7 | |
| 9 | 3 | |
| 10 | 9 | |
| 11 | Deterministic Proof Of Work. | 1 |
| 12 | Approximate Bisimulations for Constrained Discrete-Time Linear Systems | 2 |
| 13 | 13 | |
| 14 | 1 | |
| 15 | 4 | |
| 16 | 18 | |
| 17 | 65 | |
| 18 | 7 | |
| 19 | Class-Boundary Alignment for Imbalanced Dataset Learning | 223 |
| 20 | Adaptive feature-space conformal transformation for imbalanced-data learning | 71 |
About Gang Wu
Gang Wu is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 103 papers that have together received 1.8k indexed citations. Recurring topics across this work include Data Management and Algorithms (9 papers), Cloud Computing and Resource Management (8 papers) and Advanced Database Systems and Queries (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (592 citations), Artificial Intelligence (756 citations) and Media Technology (96 citations). Gang Wu has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Edward Yi Chang, King-Shy Goh, Guillermo C. Bazan, Yaofeng Chen, Fengjun Hu, John H. Helsdon, Richard D. Farley, Qibing Zhu, Ya Guo and Min Huang. Their work appears in journals such as Angewandte Chemie International Edition, Journal of Geophysical Research Atmospheres and Environmental Science & Technology.
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.