Ding Zhou
Impact in
- Artificial Intelligence top 2%
- Topic Modeling
- Neural Networks and Applications
- Mathematical Physics top 5%
- Numerical methods in inverse problems
Papers in
-
- Advanced Image Fusion Techniques 4
-
- Web Data Mining and Analysis 5
- Recommender Systems and Techniques 3
- Co-authors
- Felipe CuckerHongyuan ZhaJiang BianC. Lee GilesEugene AgichteinYandong LiuShuyi ZhengShenghuo Zhu
- Journals
- Information Sciences (1 paper)Neurocomputing (1 paper)Cancer Research (1 paper)Frontiers in Genetics (1 paper)IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (1 paper)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Ding Zhou
26 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 87
- Artificial Intelligence 713
- Mathematical Physics 185
- Statistics and Probability 158
- Computer Vision and Pattern Recognition 330
- Computational Mechanics 331
Countries citing papers authored by Ding Zhou
This map shows the geographic impact of Ding Zhou'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 Ding Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ding Zhou more than expected).
Fields of papers citing papers by Ding Zhou
This network shows the impact of papers produced by Ding Zhou. 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 Ding Zhou. The network helps show where Ding Zhou may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ding Zhou, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 2 | |
| 2 | 2025 | 2 | |
| 3 | 2024 | 0 | |
| 4 | 2022 | 0 | |
| 5 | 2022 | 5 | |
| 6 | 2021 | 3 | |
| 7 | 2021 | 39 | |
| 8 | 2021 | 1 | |
| 9 | 2020 | 1 | |
| 10 | 2020 | 16 | |
| 11 | 2018 | 2 | |
| 12 | 2010 | 33 | |
| 13 | 2009 | 16 | |
| 14 | 2009 | 129 | |
| 15 | 2008 | 98 | |
| 16 | Learning Theory: An Approximation Theory Viewpoint (Cambridge Monographs on Applied & Computational Mathematics) | 2007 | 120 |
| 17 | 2007 | 11 | |
| 18 | 2007 | 0 | |
| 19 | 2007 | 339 | |
| 20 | 2006 | 11 |
About Ding Zhou
Ding Zhou is a scholar working on Media Technology, Information Systems, Computer Vision and Pattern Recognition, Artificial Intelligence and Industrial and Manufacturing Engineering, having authored 30 papers that have together received 1.4k indexed citations. Recurring topics across this work include Web Data Mining and Analysis (5 papers), Advanced Image Fusion Techniques (4 papers), Topic Modeling (4 papers), Text and Document Classification Technologies (3 papers), Image and Object Detection Techniques (3 papers), Image Enhancement Techniques (3 papers), Recommender Systems and Techniques (3 papers) and Natural Language Processing Techniques (3 papers). The work is most often cited by research in Artificial Intelligence (713 citations), Mathematical Physics (185 citations), Statistics and Probability (158 citations), Computer Vision and Pattern Recognition (330 citations) and Computational Mechanics (331 citations). Ding Zhou has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Felipe Cucker, Hongyuan Zha, Jiang Bian, C. Lee Giles, Eugene Agichtein, Yandong Liu, Shuyi Zheng, Shenghuo Zhu, Belle L. Tseng and Xiaodan Song. Their work appears in journals such as Information Sciences, Neurocomputing, Cancer Research, Frontiers in Genetics and IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
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.