Luca Cosmo

1.0k total citations
40 papers, 558 citations indexed

About

Luca Cosmo is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Artificial Intelligence. According to data from OpenAlex, Luca Cosmo has authored 40 papers receiving a total of 558 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Computer Vision and Pattern Recognition, 10 papers in Computational Mechanics and 7 papers in Artificial Intelligence. Recurrent topics in Luca Cosmo's work include Advanced Vision and Imaging (11 papers), 3D Shape Modeling and Analysis (10 papers) and Optical measurement and interference techniques (9 papers). Luca Cosmo is often cited by papers focused on Advanced Vision and Imaging (11 papers), 3D Shape Modeling and Analysis (10 papers) and Optical measurement and interference techniques (9 papers). Luca Cosmo collaborates with scholars based in Italy, Switzerland and Germany. Luca Cosmo's co-authors include Andrea Torsello, Emanuele Rodolà, Michael M. Bronstein, Daniel Cremers, Andrea Albarelli, Filippo Bergamasco, Nassir Navab, Anees Kazi, Seyed‐Ahmad Ahmadi and Andrea Gasparetto and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Environmental Management and Pattern Recognition.

In The Last Decade

Luca Cosmo

38 papers receiving 532 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Luca Cosmo Italy 12 335 223 100 91 80 40 558
Haggai Maron Israel 7 441 1.3× 280 1.3× 183 1.8× 122 1.3× 71 0.9× 16 670
Xingyu Xie China 13 349 1.0× 255 1.1× 102 1.0× 160 1.8× 22 0.3× 40 684
Marcin Novotni Germany 9 524 1.6× 356 1.6× 127 1.3× 29 0.3× 109 1.4× 10 734
Georges‐Pierre Bonneau France 14 305 0.9× 313 1.4× 284 2.8× 46 0.5× 24 0.3× 38 642
Cengiz Öztireli Switzerland 13 461 1.4× 267 1.2× 180 1.8× 177 1.9× 30 0.4× 23 737
Aleksander Holynski United States 11 657 2.0× 152 0.7× 242 2.4× 119 1.3× 43 0.5× 19 900
Jiemin Fang China 11 391 1.2× 80 0.4× 89 0.9× 146 1.6× 39 0.5× 20 531
Jan Eric Lenssen Germany 9 204 0.6× 119 0.5× 51 0.5× 116 1.3× 24 0.3× 23 403
Bruno Raffin France 11 248 0.7× 73 0.3× 99 1.0× 29 0.3× 49 0.6× 41 446
J.A. Christensen United States 9 205 0.6× 59 0.3× 123 1.2× 47 0.5× 79 1.0× 26 504

Countries citing papers authored by Luca Cosmo

Since Specialization
Citations

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

Fields of papers citing papers by Luca Cosmo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Luca Cosmo

This figure shows the co-authorship network connecting the top 25 collaborators of Luca Cosmo. A scholar is included among the top collaborators of Luca Cosmo 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 Cosmo. Luca Cosmo 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.
Mariani, Giorgio, et al.. (2025). COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations. ARCA (Università Ca' Foscari Venezia). 1–5. 3 indexed citations
2.
Kitano, Hiroaki, et al.. (2025). Naturalistic Music Decoding from EEG Data via Latent Diffusion Models. ARCA (Università Ca' Foscari Venezia). 1–5. 1 indexed citations
3.
Mariani, Giorgio, et al.. (2024). Generalized Multi-Source Inference for Text Conditioned Music Diffusion Models. ARCA (Università Ca' Foscari Venezia). 6980–6984. 2 indexed citations
4.
Cosmo, Luca, et al.. (2023). Guided diffusion for inverse molecular design. Nature Computational Science. 3(10). 873–882. 46 indexed citations
5.
Cosmo, Luca, et al.. (2023). Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications. Medical Image Analysis. 88. 102839–102839. 5 indexed citations
6.
Mariani, Giorgio, et al.. (2023). Latent Autoregressive Source Separation. Proceedings of the AAAI Conference on Artificial Intelligence. 37(8). 9444–9452. 3 indexed citations
7.
Cosmo, Luca, et al.. (2023). GNN-LoFI: A novel graph neural network through localized feature-based histogram intersection. Pattern Recognition. 148. 110210–110210. 2 indexed citations
8.
Kazi, Anees, Luca Cosmo, Seyed‐Ahmad Ahmadi, Nassir Navab, & Michael M. Bronstein. (2022). Differentiable Graph Module (DGM) for Graph Convolutional Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(2). 1606–1617. 61 indexed citations
9.
Cosmo, Luca, et al.. (2021). Universal Spectral Adversarial Attacks for Deformable Shapes. ARCA (Università Ca' Foscari Venezia). 7 indexed citations
10.
Cosmo, Luca, et al.. (2020). A parametric analysis of discrete Hamiltonian functional maps. Computer Graphics Forum. 39(5). 103–118. 3 indexed citations
11.
Pizzol, Lisa, Alex Zabeo, Elisa Giubilato, et al.. (2018). An Information System for Brownfield Regeneration: providing customised information according to stakeholders' characteristics and needs. Journal of Environmental Management. 217. 144–156. 9 indexed citations
12.
Gasparetto, Andrea, et al.. (2018). Cross-Dataset Data Augmentation for Convolutional Neural Networks Training. ARCA (Università Ca' Foscari Venezia). abs 1409 1556. 910–915. 3 indexed citations
13.
Bergamasco, Filippo, et al.. (2018). Neighborhood-Based Recovery of Phase Unwrapping Faults. ARCA (Università Ca' Foscari Venezia). 2. 2462–2467. 1 indexed citations
14.
Cosmo, Luca, et al.. (2018). Adaptive Albedo Compensation for Accurate Phase-Shift Coding. ARCA (Università Ca' Foscari Venezia). 2. 2450–2455. 7 indexed citations
15.
Bergamasco, Filippo, Luca Cosmo, Andrea Gasparetto, Andrea Albarelli, & Andrea Torsello. (2017). Parameter-Free Lens Distortion Calibration of Central Cameras. ARCA (Università Ca' Foscari Venezia). 3867–3875. 10 indexed citations
16.
Cosmo, Luca, Emanuele Rodolà, Michael M. Bronstein, et al.. (2016). Partial Matching of Deformable Shapes. IRIS Research product catalog (Sapienza University of Rome). 21 indexed citations
17.
Lähner, Zorah, Emanuele Rodolà, Michael M. Bronstein, et al.. (2016). Matching of Deformable Shapes with Topological Noise. OpenMETU (Middle East Technical University). 10 indexed citations
18.
Cosmo, Luca, Andrea Albarelli, Filippo Bergamasco, et al.. (2016). A game-theoretical approach for joint matching of multiple feature throughout unordered images. IRIS Research product catalog (Sapienza University of Rome). 8690. 3715–3720. 3 indexed citations
19.
Bergamasco, Filippo, Luca Cosmo, Andrea Albarelli, & Andrea Torsello. (2014). Camera Calibration from Coplanar Circles. ARCA (Università Ca' Foscari Venezia). 2137–2142. 18 indexed citations
20.
Bergamasco, Filippo, Luca Cosmo, Andrea Albarelli, & Andrea Torsello. (2012). A Robust Multi-camera 3D Ellipse Fitting for Contactless Measurements. ARCA (Università Ca' Foscari Venezia). 4673. 168–175. 15 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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