Konstantin Usevich

1.3k total citations
57 papers, 684 citations indexed

About

Konstantin Usevich is a scholar working on Applied Mathematics, Computational Mechanics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Konstantin Usevich has authored 57 papers receiving a total of 684 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Applied Mathematics, 23 papers in Computational Mechanics and 19 papers in Computer Vision and Pattern Recognition. Recurrent topics in Konstantin Usevich's work include Statistical and numerical algorithms (20 papers), Sparse and Compressive Sensing Techniques (19 papers) and Image and Signal Denoising Methods (17 papers). Konstantin Usevich is often cited by papers focused on Statistical and numerical algorithms (20 papers), Sparse and Compressive Sensing Techniques (19 papers) and Image and Signal Denoising Methods (17 papers). Konstantin Usevich collaborates with scholars based in France, Belgium and United Kingdom. Konstantin Usevich's 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 and has published in prestigious journals such as Automatica, IEEE Transactions on Signal Processing and The Annals of Statistics.

In The Last Decade

Konstantin Usevich

49 papers receiving 666 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Konstantin Usevich France 16 176 174 168 117 114 57 684
Stefan Kindermann Austria 14 96 0.5× 265 1.5× 233 1.4× 46 0.4× 86 0.8× 55 959
Mátyás A. Sustik United States 7 110 0.6× 228 1.3× 233 1.4× 125 1.1× 29 0.3× 10 749
Ricardo D. Fierro United States 12 200 1.1× 126 0.7× 116 0.7× 77 0.7× 25 0.2× 19 567
Zhigang Jia China 15 284 1.6× 418 2.4× 182 1.1× 89 0.8× 91 0.8× 52 858
Emanuel Guariglia Italy 12 161 0.9× 200 1.1× 45 0.3× 46 0.4× 55 0.5× 19 787
Venkat Chandrasekaran United States 11 34 0.2× 82 0.5× 214 1.3× 90 0.8× 18 0.2× 27 589
Sam Efromovich United States 18 148 0.8× 192 1.1× 74 0.4× 24 0.2× 29 0.3× 86 1.1k
Amina Chebira United States 13 224 1.3× 297 1.7× 183 1.1× 165 1.4× 78 0.7× 30 848
Jiasong Wu China 19 45 0.3× 707 4.1× 66 0.4× 143 1.2× 116 1.0× 78 1.1k
Luoqing Li China 23 201 1.1× 770 4.4× 288 1.7× 191 1.6× 506 4.4× 100 1.6k

Countries citing papers authored by Konstantin Usevich

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Usevich, Konstantin, Jonathan Gillard, Philippe Dreesen, & Ivan Markovsky. (2025). Structured Nuclear Norm Matrix Completion: Guaranteeing Exact Recovery via Block‐Column Scaling. Numerical Linear Algebra with Applications. 32(4).
2.
Clausel, Marianne, et al.. (2024). The Barycenter in Free Nilpotent Lie Groups and Its Application to Iterated-Integrals Signatures. SPIRE - Sciences Po Institutional REpository. 8(3). 519–552.
3.
4.
Tremblay, Nicolas, Simon Barthelmé, Konstantin Usevich, & Pierre‐Olivier Amblard. (2023). Extended L-ensembles: A new representation for determinantal point processes. The Annals of Applied Probability. 33(1). 3 indexed citations
5.
Barthelmé, Simon, Nicolas Tremblay, Konstantin Usevich, & Pierre‐Olivier Amblard. (2023). Determinantal point processes in the flat limit. Bernoulli. 29(2). 3 indexed citations
6.
Barthelmé, Simon, Pierre‐Olivier Amblard, Nicolas Tremblay, & Konstantin Usevich. (2023). Gaussian process regression in the flat limit. The Annals of Statistics. 51(6). 1 indexed citations
7.
Usevich, Konstantin, et al.. (2022). Constrained Cramér–Rao bounds for reconstruction problems formulated as coupled canonical polyadic decompositions. Signal Processing. 198. 108573–108573. 2 indexed citations
8.
Borsoi, Ricardo Augusto, et al.. (2021). Coupled Tensor Decomposition for Hyperspectral and Multispectral Image Fusion With Inter-Image Variability. IEEE Journal of Selected Topics in Signal Processing. 15(3). 702–717. 43 indexed citations
9.
Usevich, Konstantin, et al.. (2021). A tensor-based approach for training flexible neural networks. 2021 55th Asilomar Conference on Signals, Systems, and Computers. 1673–1677.
10.
Barthelmé, Simon & Konstantin Usevich. (2019). Spectral properties of kernel matrices in the flat limit. arXiv (Cornell University). 8 indexed citations
11.
Gillard, Jonathan & Konstantin Usevich. (2018). Structured low-rank matrix completion for forecasting in time series\n analysis. arXiv (Cornell University). 16 indexed citations
12.
Usevich, Konstantin & Ivan Markovsky. (2015). Adjusted least squares fitting of algebraic hypersurfaces. Linear Algebra and its Applications. 502. 243–274. 5 indexed citations
13.
Marchı, Stefano De & Konstantin Usevich. (2014). On certain multivariate Vandermonde determinants whose variables separate. Linear Algebra and its Applications. 449. 17–27. 4 indexed citations
14.
Usevich, Konstantin & Ivan Markovsky. (2013). Variable projection methods for approximate (greatest) common divisor\n computations. arXiv (Cornell University). 8 indexed citations
15.
Usevich, Konstantin & Ivan Markovsky. (2013). Variable projection for affinely structured low-rank approximation in weighted 2-norms. Journal of Computational and Applied Mathematics. 272. 430–448. 25 indexed citations
16.
Markovsky, Ivan & Konstantin Usevich. (2013). Software for weighted structured low-rank approximation. Journal of Computational and Applied Mathematics. 256. 278–292. 43 indexed citations
17.
Golyandina, Nina, et al.. (2012). Measuring Gene Expression Noise in Early Drosophila Embryos: Nucleus-to-nucleus Variability. Procedia Computer Science. 9. 373–382. 11 indexed citations
18.
Holloway, David M., Francisco J. P. Lopes, Luciano da Fontoura Costa, et al.. (2011). Gene Expression Noise in Spatial Patterning: hunchback Promoter Structure Affects Noise Amplitude and Distribution in Drosophila Segmentation. PLoS Computational Biology. 7(2). e1001069–e1001069. 61 indexed citations
19.
Usevich, Konstantin. (2010). On signal and extraneous roots in singular spectrum analysis. Statistics and Its Interface. 3(3). 281–295. 11 indexed citations
20.
Golyandina, Nina, Konstantin Usevich, & Igor V. Florinsky. (2007). Filtering of Digital Terrain Models by Two-Dimensional Singular Spectrum Analysis. International Journal of Ecology & Development. 8. 81–94. 17 indexed citations

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.

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