André Ferrari

936 total citations
38 papers, 510 citations indexed

About

André Ferrari is a scholar working on Computational Mechanics, Astronomy and Astrophysics and Computer Vision and Pattern Recognition. According to data from OpenAlex, André Ferrari has authored 38 papers receiving a total of 510 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computational Mechanics, 9 papers in Astronomy and Astrophysics and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in André Ferrari's work include Sparse and Compressive Sensing Techniques (10 papers), Adaptive optics and wavefront sensing (7 papers) and Image and Signal Denoising Methods (7 papers). André Ferrari is often cited by papers focused on Sparse and Compressive Sensing Techniques (10 papers), Adaptive optics and wavefront sensing (7 papers) and Image and Signal Denoising Methods (7 papers). André Ferrari collaborates with scholars based in France, United States and Canada. André Ferrari's co-authors include Cédric Richard, Ali H. Sayed, Roula Nassif, C. Aimé, Rémi Soummer, Laurent Jolıssaınt, David Mary, Jean‐Yves Tourneret, Anand Sivaramakrishnan and Lotfi Chaâri and has published in prestigious journals such as The Astrophysical Journal, IEEE Transactions on Image Processing and IEEE Transactions on Signal Processing.

In The Last Decade

André Ferrari

35 papers receiving 500 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
André Ferrari France 13 155 108 99 95 79 38 510
A. Ferrari France 8 102 0.7× 132 1.2× 48 0.5× 38 0.4× 55 0.7× 40 417
Keir Mierle Canada 7 401 2.6× 39 0.4× 99 1.0× 10 0.1× 117 1.5× 9 721
W. Macy United States 11 236 1.5× 87 0.8× 61 0.6× 70 0.7× 9 0.1× 43 744
J. L. Yen Canada 14 229 1.5× 95 0.9× 118 1.2× 25 0.3× 28 0.4× 50 815
Henry Leung Canada 19 572 3.7× 38 0.4× 72 0.7× 14 0.1× 269 3.4× 57 1.3k
Jubo Zhu China 13 23 0.1× 47 0.4× 136 1.4× 126 1.3× 16 0.2× 88 677
Sebastian Egner Germany 14 182 1.2× 246 2.3× 22 0.2× 14 0.1× 89 1.1× 63 1.4k
Ajit Kembhavi India 19 836 5.4× 42 0.4× 64 0.6× 32 0.3× 241 3.1× 79 1.2k
K. A. Lukin Ukraine 13 52 0.3× 200 1.9× 30 0.3× 14 0.1× 9 0.1× 163 801
François Lanusse France 18 550 3.5× 84 0.8× 51 0.5× 12 0.1× 188 2.4× 38 769

Countries citing papers authored by André Ferrari

Since Specialization
Citations

This map shows the geographic impact of André Ferrari'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 André Ferrari with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites André Ferrari more than expected).

Fields of papers citing papers by André Ferrari

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by André Ferrari. 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 André Ferrari. The network helps show where André Ferrari may publish in the future.

Co-authorship network of co-authors of André Ferrari

This figure shows the co-authorship network connecting the top 25 collaborators of André Ferrari. A scholar is included among the top collaborators of André Ferrari 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 André Ferrari. André Ferrari 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.
Ferrari, André, et al.. (2022). Online change-point detection with kernels. Pattern Recognition. 133. 109022–109022. 9 indexed citations
2.
Nassif, Roula, Cédric Richard, André Ferrari, & Ali H. Sayed. (2017). Diffusion LMS for Multitask Problems With Local Linear Equality Constraints. IEEE Transactions on Signal Processing. 65(19). 4979–4993. 45 indexed citations
3.
Ferrari, André, et al.. (2016). Nonlinear Unmixing of Hyperspectral Data With Vector-Valued Kernel Functions. IEEE Transactions on Image Processing. 26(1). 340–354. 34 indexed citations
4.
Thiébaut, Éric, Laurent Denis, Laurent M. Mugnier, et al.. (2016). Fast and robust exo-planet detection in multi-spectral, multi-temporal data. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9909. 990957–990957. 1 indexed citations
5.
Nassif, Roula, Cédric Richard, André Ferrari, & Ali H. Sayed. (2016). Multitask Diffusion Adaptation OverAsynchronous Networks. IEEE Transactions on Signal Processing. 64(11). 2835–2850. 46 indexed citations
6.
Tourneret, Jean‐Yves, et al.. (2016). Improving Spacecraft Health Monitoring with Automatic Anomaly Detection Techniques. 38 indexed citations
7.
Mary, David, et al.. (2014). Dimension Reduction for Hypothesis Testing in Worst-Case Scenarios. IEEE Transactions on Signal Processing. 62(22). 5973–5986. 3 indexed citations
8.
Mary, David, et al.. (2014). Subspace learning in minimax detection. 33. 3062–3066.
9.
Ferrari, André, et al.. (2014). GLUP: Yet another algorithm for blind unmixing of hyperspectral data. 41. 1–4. 5 indexed citations
10.
Chen, Jie, Cédric Richard, André Ferrari, & Paul Honeiné. (2013). Nonlinear unmixing of hyperspectral data with partially linear least-squares support vector regression. HAL (Le Centre pour la Communication Scientifique Directe). 9. 2174–2178. 7 indexed citations
11.
Mary, David, et al.. (2011). Sparsity-based composite detection tests. application to astrophysical hyperspectral data. European Signal Processing Conference. 1909–1913. 5 indexed citations
12.
Ferrari, André, et al.. (2010). Classical adaptive optics: disturbance rejection control. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7736. 77364F–77364F. 1 indexed citations
13.
Soummer, Rémi, Laurent Pueyo, André Ferrari, et al.. (2009). APODIZED PUPIL LYOT CORONAGRAPHS FOR ARBITRARY APERTURES. II. THEORETICAL PROPERTIES AND APPLICATION TO EXTREMELY LARGE TELESCOPES. The Astrophysical Journal. 695(1). 695–706. 32 indexed citations
14.
Ferrari, André, C. Aimé, & Rémi Soummer. (2009). ANALYTICAL COMPUTATION OF THE LYOT CORONAGRAPH RESPONSE TO AN EXTENDED SOURCE. The Astrophysical Journal. 708(1). 218–223. 4 indexed citations
15.
Ferrari, André, Rémi Soummer, & C. Aimé. (2007). An introduction to stellar coronagraphy. Comptes Rendus Physique. 8(3-4). 277–287. 11 indexed citations
16.
Soummer, Rémi, André Ferrari, C. Aimé, & Laurent Jolıssaınt. (2007). Speckle Noise and Dynamic Range in Coronagraphic Images. The Astrophysical Journal. 669(1). 642–656. 67 indexed citations
17.
Soummer, Rémi, C. Aimé, André Ferrari, et al.. (2005). Apodized Pupil Lyot Coronagraphs: Concepts and application to the Gemini Planet Imager. Proceedings of the International Astronomical Union. 1(C200). 367–372. 3 indexed citations
18.
Tourneret, Jean‐Yves, André Ferrari, & Gérard Letac. (2002). Changepoint detection in multivariate Poisson distributions. IEEE International Conference on Acoustics Speech and Signal Processing. II–1573. 2 indexed citations
19.
Ferrari, André & C. Aimé. (2001). Étude statistique de la détection d'exo-planètes en imagerie courte pose.
20.
Ferrari, André & E.A. Galperin. (1993). Numerical experiments with one-dimensional adaptive cubic algorithm. Computers & Mathematics with Applications. 25(10-11). 47–56. 12 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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