Matteo Santoro

969 total citations
29 papers, 512 citations indexed

About

Matteo Santoro is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Matteo Santoro has authored 29 papers receiving a total of 512 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computer Vision and Pattern Recognition, 7 papers in Artificial Intelligence and 5 papers in Computational Mechanics. Recurrent topics in Matteo Santoro's work include Sparse and Compressive Sensing Techniques (5 papers), Parkinson's Disease Mechanisms and Treatments (4 papers) and Nerve injury and regeneration (3 papers). Matteo Santoro is often cited by papers focused on Sparse and Compressive Sensing Techniques (5 papers), Parkinson's Disease Mechanisms and Treatments (4 papers) and Nerve injury and regeneration (3 papers). Matteo Santoro collaborates with scholars based in Italy, United States and United Kingdom. Matteo Santoro's co-authors include Lorenzo Rosasco, Alessandro Verri, Sofia Mosci, Silvia Villa, Peter Teismann, Gernot Riedel, John V. Forrester, Heather L. Martin, Sarah Mustafa and Guglielmo Tamburrini and has published in prestigious journals such as Journal of Applied Physics, Brain Research and IEEE Transactions on Image Processing.

In The Last Decade

Matteo Santoro

27 papers receiving 497 citations

Peers

Matteo Santoro
Comparison fields: 5 of 104
  • Computer Vision and Pattern Recognition 101
  • Molecular Biology 92
  • Neurology 87
  • Neurology 79
  • Artificial Intelligence 71
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Citations per field, relative to Matteo Santoro
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Citations per year, relative to Matteo Santoro
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Countries citing papers authored by Matteo Santoro

Since Specialization
Citations

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

Fields of papers citing papers by Matteo Santoro

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matteo Santoro

This figure shows the co-authorship network connecting the top 25 collaborators of Matteo Santoro. A scholar is included among the top collaborators of Matteo Santoro 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 Matteo Santoro. Matteo Santoro 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
# Work Indexed citations
1 0
2 19
3 41
4 82
5 6
6 15
7 16
8 10
9 14
10 3
11
A Regularization Approach to Nonlinear Variable Selection
9
12
Iterative Projection Methods for Structured Sparsity Regularization
63
13 6
14
Unsupervised Learning of Behavioural Patterns for Video-Surveillance
2
15 6
16 8
17 3
18 3
19 23
20 29

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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