Marco Prato

1.2k total citations
57 papers, 676 citations indexed

About

Marco Prato is a scholar working on Computational Mechanics, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Marco Prato has authored 57 papers receiving a total of 676 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Computational Mechanics, 20 papers in Computer Vision and Pattern Recognition and 14 papers in Artificial Intelligence. Recurrent topics in Marco Prato's work include Sparse and Compressive Sensing Techniques (27 papers), Numerical methods in inverse problems (13 papers) and Image and Signal Denoising Methods (12 papers). Marco Prato is often cited by papers focused on Sparse and Compressive Sensing Techniques (27 papers), Numerical methods in inverse problems (13 papers) and Image and Signal Denoising Methods (12 papers). Marco Prato collaborates with scholars based in Italy, United States and United Kingdom. Marco Prato's co-authors include Silvia Bonettini, Luca Zanni, Federica Porta, Michele Piana, Anna Maria Massone, Eduard P. Kontar, A. G. Emslie, Ignace Loris, Jean‐Christophe Pesquet and Émilie Chouzenoux and has published in prestigious journals such as The Astrophysical Journal, NeuroImage and Atmospheric Environment.

In The Last Decade

Marco Prato

52 papers receiving 627 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marco Prato Italy 17 220 160 123 102 97 57 676
Audrey Repetti United Kingdom 11 305 1.4× 186 1.2× 113 0.9× 51 0.5× 74 0.8× 35 532
Rosemary A. Renaut United States 24 297 1.4× 190 1.2× 61 0.5× 47 0.5× 152 1.6× 92 1.3k
D. Picard France 11 96 0.4× 290 1.8× 73 0.6× 79 0.8× 83 0.9× 25 732
Riccardo March Italy 17 199 0.9× 320 2.0× 113 0.9× 25 0.2× 84 0.9× 44 815
Stefan Kunis Germany 15 281 1.3× 203 1.3× 25 0.2× 27 0.3× 59 0.6× 40 843
Satyanad Kichenassamy France 18 180 0.8× 615 3.8× 185 1.5× 82 0.8× 397 4.1× 53 1.6k
Walter Schempp Germany 12 198 0.9× 101 0.6× 41 0.3× 53 0.5× 97 1.0× 94 770
Shingyu Leung Hong Kong 18 410 1.9× 119 0.7× 20 0.2× 41 0.4× 182 1.9× 66 1.1k
Anil N. Hirani United States 14 469 2.1× 222 1.4× 37 0.3× 22 0.2× 43 0.4× 30 894
Philippe Tchamitchian France 15 53 0.2× 411 2.6× 57 0.5× 57 0.6× 243 2.5× 29 1.3k

Countries citing papers authored by Marco Prato

Since Specialization
Citations

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

Fields of papers citing papers by Marco Prato

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marco Prato

This figure shows the co-authorship network connecting the top 25 collaborators of Marco Prato. A scholar is included among the top collaborators of Marco Prato 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 Marco Prato. Marco Prato 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.
Bonettini, Silvia, et al.. (2024). A nested primal–dual iterated Tikhonov method for regularized convex optimization. Computational Optimization and Applications. 91(2). 357–395.
2.
Bonettini, Silvia, et al.. (2024). A new proximal heavy ball inexact line-search algorithm. Computational Optimization and Applications. 88(2). 525–565.
3.
Bonettini, Silvia, et al.. (2023). On an iteratively reweighted linesearch based algorithm for nonconvex composite optimization. Inverse Problems. 39(6). 64001–64001. 1 indexed citations
4.
Bonettini, Silvia, et al.. (2023). An abstract convergence framework with application to inertial inexact forward–backward methods. Computational Optimization and Applications. 84(2). 319–362. 1 indexed citations
5.
Prato, Marco, et al.. (2023). Denoising Diffusion Models on Model-Based Latent Space. Algorithms. 16(11). 501–501.
6.
Piccolomini, Elena Loli, et al.. (2023). CTprintNet: An Accurate and Stable Deep Unfolding Approach for Few-View CT Reconstruction. Algorithms. 16(6). 270–270. 3 indexed citations
7.
Prato, Marco, et al.. (2023). DCT-Former: Efficient Self-Attention with Discrete Cosine Transform. Journal of Scientific Computing. 94(3). 18 indexed citations
8.
Bonettini, Silvia, et al.. (2022). Explainable bilevel optimization: An application to the Helsinki deblur challenge. Inverse Problems and Imaging. 17(5). 925–950. 8 indexed citations
9.
Lassas, Matti, et al.. (2020). Deep neural networks for inverse problems with pseudodifferential\n operators: an application to limited-angle tomography. arXiv (Cornell University). 15 indexed citations
10.
Chouzenoux, Émilie, et al.. (2019). Deep unfolding of a proximal interior point method for image restoration. Inverse Problems. 36(3). 34005–34005. 72 indexed citations
11.
Bonettini, Silvia, et al.. (2018). A block coordinate variable metric linesearch based proximal gradient method. Computational Optimization and Applications. 71(1). 5–52. 18 indexed citations
12.
Bonettini, Silvia, et al.. (2016). A cyclic block coordinate descent method with generalized gradient projections. Applied Mathematics and Computation. 286. 288–300. 10 indexed citations
13.
Bonettini, Silvia & Marco Prato. (2014). A new general framework for gradient projection methods. arXiv (Cornell University). 3 indexed citations
14.
Carbillet, M., Andrea Camera, O. Chesneau, et al.. (2013). Deconvolution-based super-resolution for post-adaptive-optics data. IRIS UNIMORE (University of Modena and Reggio Emilia). 104.
15.
Bonettini, Silvia, et al.. (2013). A New Semiblind Deconvolution Approach for Fourier-Based Image Restoration: An Application in Astronomy. SIAM Journal on Imaging Sciences. 6(3). 1736–1757. 20 indexed citations
16.
Prato, Marco, Roberto Cavicchioli, Luca Zanni, Patrizia Boccacci, & M. Bertero. (2012). Efficient deconvolution methods for astronomical imaging: algorithms and IDL-GPU codes. Springer Link (Chiba Institute of Technology). 35 indexed citations
17.
Perrone, M. R., et al.. (2011). Composition of fine and coarse particles in a coastal site of the central Mediterranean: Carbonaceous species contributions. Atmospheric Environment. 45(39). 7470–7477. 51 indexed citations
18.
Prato, Marco, Stefania Favilla, Luca Zanni, Carlo Adolfo Porro, & Patrizia Baraldi. (2011). A regularization algorithm for decoding perceptual temporal profiles from fMRI data. NeuroImage. 56(1). 258–267. 16 indexed citations
19.
Piana, Michele, Anna Maria Massone, G. J. Hurford, et al.. (2007). Electron Flux Spectral Imaging of Solar Flares through Regularized Analysis of Hard X‐Ray Source Visibilities. The Astrophysical Journal. 665(1). 846–855. 30 indexed citations
20.
Massone, Anna Maria, A. G. Emslie, Eduard P. Kontar, et al.. (2004). Anisotropic Bremsstrahlung Emission and the Form of Regularized Electron Flux Spectra in Solar Flares. The Astrophysical Journal. 613(2). 1233–1240. 32 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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