Luca Moschella

582 total citations
2 papers, 5 citations indexed

About

Luca Moschella is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Artificial Intelligence. According to data from OpenAlex, Luca Moschella has authored 2 papers receiving a total of 5 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Computer Vision and Pattern Recognition, 1 paper in Computational Mechanics and 1 paper in Artificial Intelligence. Recurrent topics in Luca Moschella's work include Computer Graphics and Visualization Techniques (1 paper), Advanced Vision and Imaging (1 paper) and Domain Adaptation and Few-Shot Learning (1 paper). Luca Moschella is often cited by papers focused on Computer Graphics and Visualization Techniques (1 paper), Advanced Vision and Imaging (1 paper) and Domain Adaptation and Few-Shot Learning (1 paper). Luca Moschella collaborates with scholars based in Italy, France and United States. Luca Moschella's co-authors include Luca Cosmo, Maks Ovsjanikov, Leonidas Guibas, Emanuele Rodolà, Matteo Boschini, Simone Melzi, Simone Calderara and Or Litany and has published in prestigious journals such as Pattern Recognition Letters and Computer Graphics Forum.

In The Last Decade

Luca Moschella

2 papers receiving 5 citations

Peers — A (Enhanced Table)

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

Name	h					Papers	Cites
Luca Moschella Italy	2	3	2	2	2	2	5
Tuomas Kynkäänniemi Finland	2	2 0.7×	2 1.0×		2 1.0×	3	5
Marc Szafraniec United Kingdom	2	3 1.0×	2 1.0×			2	3
J. Hu China	2	3 1.0×	2 1.0×			7	4
C. Liu China	2	3 1.0×		2 1.0×		5	7
Ruiqi Li China	2	3 1.0×		2 1.0×		3	4
H. S. Chen China	2	3 1.0×		2 1.0×	1 0.5×	3	10
Mayur Sonawane India	2	3 1.0×		2 1.0×		5	5
Z. Wu China	4	4 1.3×		2 1.0×		10	17
K. K. Kwan United States	2	3 1.0×	3 1.5×	2 1.0×		2	7
Isaac Noble United States	2	3 1.0×		3 1.5×		3	6

Countries citing papers authored by Luca Moschella

Since Specialization

Citations

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

Fields of papers citing papers by Luca Moschella

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Luca Moschella

This figure shows the co-authorship network connecting the top 25 collaborators of Luca Moschella. A scholar is included among the top collaborators of Luca Moschella 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 Luca Moschella. Luca Moschella is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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2 of 2 papers shown

1.

Boschini, Matteo, et al.. (2024). Latent spectral regularization for continual learning. Pattern Recognition Letters. 184. 119–125. 2 indexed citations

2.

Moschella, Luca, Simone Melzi, Luca Cosmo, et al.. (2022). Learning Spectral Unions of Partial Deformable 3D Shapes. Computer Graphics Forum. 41(2). 407–417. 3 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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