Xiangyu Chang
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Health, Toxicology and Mutagenesis top 5%
- Computational Mechanics top 5%
- Atmospheric Science top 10%
- Topics
- Sparse and Compressive Sensing Techniques (14 papers)Complex Network Analysis Techniques (10 papers)Advanced Clustering Algorithms Research (6 papers)
- Cited by
- Computational MathematicsHealth, Toxicology and MutagenesisComputer Vision and Pattern Recognition
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Xiangyu Chang
61 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 137
- Artificial Intelligence 312
- Computer Vision and Pattern Recognition 290
- Health, Toxicology and Mutagenesis 258
- Computational Mechanics 255
- Atmospheric Science 213
Countries citing papers authored by Xiangyu Chang
This map shows the geographic impact of Xiangyu Chang'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 Xiangyu Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiangyu Chang more than expected).
Fields of papers citing papers by Xiangyu Chang
This network shows the impact of papers produced by Xiangyu Chang. 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 Xiangyu Chang. The network helps show where Xiangyu Chang may publish in the future.
Co-authorship network of co-authors of Xiangyu Chang
This figure shows the co-authorship network connecting the top 25 collaborators of Xiangyu Chang. A scholar is included among the top collaborators of Xiangyu Chang 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 Xiangyu Chang. Xiangyu Chang 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 | 0 | |
| 5 | 25 | |
| 6 | 7 | |
| 7 | 90 | |
| 8 | 12 | |
| 9 | 7 | |
| 10 | 69 | |
| 11 | 3 | |
| 12 | Substantial emission reductions from Chinese power plants after the introduction of ultra-low emissions standardsbreakdown → | 333 |
| 13 | 15 | |
| 14 | 10 | |
| 15 | 4 | |
| 16 | 11 | |
| 17 | Distributed semi-supervised learning with kernel ridge regression | 40 |
| 18 | AN EMPIRICAL RESEARCH ON TECHNOSTRESS CREATORS AND END-USER PERFORMANCE: THE MEDIATING ROLES OF AFFECTIVE ATTITUDES | 2 |
| 19 | 1 | |
| 20 | 5 |
About Xiangyu Chang
Xiangyu Chang is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computational Mechanics, having authored 70 papers that have together received 1.6k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (14 papers), Complex Network Analysis Techniques (10 papers) and Advanced Clustering Algorithms Research (6 papers). The work is most often cited by research in Computational Mathematics (32 citations), Health, Toxicology and Mutagenesis (258 citations) and Computer Vision and Pattern Recognition (290 citations). Xiangyu Chang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Yao Wang, Zongben Xu, Yong Liang, Hai Zhang, Xin Bo, Zhifu Mi, Shouyang Wang, Xiaoda Xue, Ling Tang and Jiabao Qu. Their work appears in journals such as PLoS ONE, Scientific Reports and IEEE Transactions on Signal 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.