Hao Shen
- Computer Vision and Pattern Recognition top 5%
- Electrical and Electronic Engineering
- Industrial and Manufacturing Engineering top 2%
- Mechanical Engineering top 10%
- Signal Processing top 5%
- Co-authors
- Patrick M. PilarskiJohannes GüntherKlaus DiepoldMartin KleinsteuberShuxiao LiHongxing ChangXian WeiQinru Qiu
- Topics
- Sparse and Compressive Sensing Techniques (12 papers)Blind Source Separation Techniques (9 papers)Image and Signal Denoising Methods (6 papers)
- Journals
- Applied Physics LettersIEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Image Processing
- Partner nations
- GermanyChinaUnited States
In The Last Decade
Hao Shen
43 papers receiving 972 citations
Peers
Comparison fields: 5 of 116
- Computer Vision and Pattern Recognition 259
- Electrical and Electronic Engineering 217
- Industrial and Manufacturing Engineering 185
- Mechanical Engineering 177
- Signal Processing 142
Countries citing papers authored by Hao Shen
This map shows the geographic impact of Hao Shen'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 Hao Shen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hao Shen more than expected).
Fields of papers citing papers by Hao Shen
This network shows the impact of papers produced by Hao Shen. 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 Hao Shen. The network helps show where Hao Shen may publish in the future.
Co-authorship network of co-authors of Hao Shen
This figure shows the co-authorship network connecting the top 25 collaborators of Hao Shen. A scholar is included among the top collaborators of Hao Shen 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 Hao Shen. Hao Shen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 12 | |
| 3 | 0 | |
| 4 | 12 | |
| 5 | 3 | |
| 6 | 15 | |
| 7 | 11 | |
| 8 | 1 | |
| 9 | 1 | |
| 10 | 135 | |
| 11 | 6 | |
| 12 | 1 | |
| 13 | 84 | |
| 14 | 9 | |
| 15 | 84 | |
| 16 | 2 | |
| 17 | 45 | |
| 18 | 18 | |
| 19 | 3 | |
| 20 | 23 |
About Hao Shen
Hao Shen is a scholar working on Acoustics and Ultrasonics, Signal Processing and Computer Vision and Pattern Recognition, having authored 45 papers that have together received 1.0k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (12 papers), Blind Source Separation Techniques (9 papers) and Image and Signal Denoising Methods (6 papers). The work is most often cited by research in Acoustics and Ultrasonics (36 citations), Computational Mathematics (18 citations) and Industrial and Manufacturing Engineering (185 citations). Hao Shen has collaborated with scholars based in Germany, China and United States. Frequent co-authors include Patrick M. Pilarski, Johannes Günther, Klaus Diepold, Martin Kleinsteuber, Shuxiao Li, Hongxing Chang, Xian Wei, Qinru Qiu, Ying Tan and Jun Lu. Their work appears in journals such as Applied Physics Letters, IEEE Transactions on Pattern Analysis and Machine Intelligence 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.