Konstantin Usevich
- Applied Mathematics top 5%
- Computer Vision and Pattern Recognition top 5%
- Computational Mechanics top 5%
- Signal Processing top 5%
- Media Technology top 5%
- Co-authors
- Ivan MarkovskyPierre ComonNina GolyandinaDavid BrieAnton KorobeynikovAlexander ShlemovAlexander V. SpirovRicardo Augusto Borsoi
- Topics
- Statistical and numerical algorithms (20 papers)Sparse and Compressive Sensing Techniques (19 papers)Image and Signal Denoising Methods (17 papers)
- Partner nations
- FranceBelgiumUnited Kingdom
In The Last Decade
Konstantin Usevich
49 papers receiving 666 citations
Peers
Comparison fields: 5 of 91
- Applied Mathematics 176
- Computer Vision and Pattern Recognition 174
- Computational Mechanics 168
- Signal Processing 117
- Media Technology 114
Countries citing papers authored by Konstantin Usevich
This map shows the geographic impact of Konstantin Usevich'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 Konstantin Usevich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Konstantin Usevich more than expected).
Fields of papers citing papers by Konstantin Usevich
This network shows the impact of papers produced by Konstantin Usevich. 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 Konstantin Usevich. The network helps show where Konstantin Usevich may publish in the future.
Co-authorship network of co-authors of Konstantin Usevich
This figure shows the co-authorship network connecting the top 25 collaborators of Konstantin Usevich. A scholar is included among the top collaborators of Konstantin Usevich 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 Konstantin Usevich. Konstantin Usevich 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 | 3 | |
| 5 | 3 | |
| 6 | 1 | |
| 7 | 2 | |
| 8 | 43 | |
| 9 | 0 | |
| 10 | 8 | |
| 11 | 16 | |
| 12 | 5 | |
| 13 | 4 | |
| 14 | 8 | |
| 15 | 25 | |
| 16 | 43 | |
| 17 | 11 | |
| 18 | 61 | |
| 19 | 11 | |
| 20 | Filtering of Digital Terrain Models by Two-Dimensional Singular Spectrum Analysis | 17 |
About Konstantin Usevich
Konstantin Usevich is a scholar working on Computational Mathematics, Applied Mathematics and Computational Mechanics, having authored 57 papers that have together received 684 indexed citations. Recurring topics across this work include Statistical and numerical algorithms (20 papers), Sparse and Compressive Sensing Techniques (19 papers) and Image and Signal Denoising Methods (17 papers). The work is most often cited by research in Computational Mathematics (73 citations), Applied Mathematics (176 citations) and Media Technology (114 citations). Konstantin Usevich has collaborated with scholars based in France, Belgium and United Kingdom. Frequent co-authors include Ivan Markovsky, Pierre Comon, Nina Golyandina, David Brie, Anton Korobeynikov, Alexander Shlemov, Alexander V. Spirov, Ricardo Augusto Borsoi, Frank Gauterin and Francisco J. P. Lopes. Their work appears in journals such as Automatica, IEEE Transactions on Signal Processing and The Annals of Statistics.
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.