Donghua Wang
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Computer Networks and Communications
- Control and Systems Engineering
- Signal Processing top 10%
- Topics
- Adversarial Robustness in Machine Learning (11 papers)Generative Adversarial Networks and Image Synthesis (3 papers)Fault Detection and Control Systems (3 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Geoscience and Remote SensingIEEE Transactions on Image Processing
- Partner nations
- ChinaUnited States
In The Last Decade
Donghua Wang
22 papers receiving 241 citations
Peers
Comparison fields: 5 of 55
- Artificial Intelligence 179
- Computer Vision and Pattern Recognition 63
- Computer Networks and Communications 57
- Control and Systems Engineering 55
- Signal Processing 44
Countries citing papers authored by Donghua Wang
This map shows the geographic impact of Donghua Wang'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 Donghua Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Donghua Wang more than expected).
Fields of papers citing papers by Donghua Wang
This network shows the impact of papers produced by Donghua Wang. 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 Donghua Wang. The network helps show where Donghua Wang may publish in the future.
Co-authorship network of co-authors of Donghua Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Donghua Wang. A scholar is included among the top collaborators of Donghua Wang 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 Donghua Wang. Donghua Wang 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 | 1 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 14 | |
| 7 | 13 | |
| 8 | 2 | |
| 9 | 4 | |
| 10 | 4 | |
| 11 | 25 | |
| 12 | 2 | |
| 13 | 4 | |
| 14 | 1 | |
| 15 | 1 | |
| 16 | 9 | |
| 17 | 5 | |
| 18 | 2 | |
| 19 | 67 | |
| 20 | 1 |
About Donghua Wang
Donghua Wang is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition, having authored 25 papers that have together received 245 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (11 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and Fault Detection and Control Systems (3 papers). The work is most often cited by research in Artificial Intelligence (179 citations), Signal Processing (44 citations) and Hardware and Architecture (26 citations). Donghua Wang has collaborated with scholars based in China and United States. Frequent co-authors include Wen Yao, Tingsong Jiang, Xiaoqian Chen, Yunmin Zhu, Jie Zhou, Enbin Song, Yingting Luo, Xiaoya Zhang, Zhiqiang Gong and Rangding Wang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Geoscience and Remote Sensing and IEEE Transactions on Image Processing.
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.