Giuliano Grossi

1.4k total citations
49 papers, 461 citations indexed

About

Giuliano Grossi is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Giuliano Grossi has authored 49 papers receiving a total of 461 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computer Vision and Pattern Recognition, 12 papers in Artificial Intelligence and 9 papers in Computational Mechanics. Recurrent topics in Giuliano Grossi's work include Sparse and Compressive Sensing Techniques (8 papers), Face recognition and analysis (7 papers) and ECG Monitoring and Analysis (6 papers). Giuliano Grossi is often cited by papers focused on Sparse and Compressive Sensing Techniques (8 papers), Face recognition and analysis (7 papers) and ECG Monitoring and Analysis (6 papers). Giuliano Grossi collaborates with scholars based in Italy, France and United States. Giuliano Grossi's co-authors include Raffaella Lanzarotti, Jianyi Lin, Vittorio Cuculo, Giuseppe Boccignone, Alessandro D’Amelio, Donatello Conte, Francesca Odone, Nicoletta Noceti, Paolo Napoletano and Federico Pedersini and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Giuliano Grossi

47 papers receiving 452 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Giuliano Grossi Italy 11 147 122 117 74 70 49 461
Shuting Xu China 14 102 0.7× 47 0.4× 93 0.8× 166 2.2× 34 0.5× 47 599
Jayanta Mukhopadhyay India 12 118 0.8× 226 1.9× 39 0.3× 78 1.1× 52 0.7× 101 532
Raffaella Lanzarotti Italy 14 153 1.0× 302 2.5× 114 1.0× 35 0.5× 104 1.5× 42 602
Puneet Gupta India 16 239 1.6× 237 1.9× 165 1.4× 92 1.2× 294 4.2× 47 690
Seedahmed S. Mahmoud Australia 13 142 1.0× 50 0.4× 130 1.1× 72 1.0× 52 0.7× 57 487
Allam Jaya Prakash India 15 157 1.1× 160 1.3× 177 1.5× 115 1.6× 43 0.6× 30 633
Akira Tanaka Japan 10 117 0.8× 66 0.5× 81 0.7× 50 0.7× 28 0.4× 83 404
Cristhian Potes United States 9 73 0.5× 47 0.4× 109 0.9× 49 0.7× 103 1.5× 17 536
Ramchandra Manthalkar India 12 38 0.3× 191 1.6× 40 0.3× 64 0.9× 36 0.5× 51 497
Gwo‐Jen Jan Taiwan 6 228 1.6× 133 1.1× 171 1.5× 24 0.3× 85 1.2× 16 525

Countries citing papers authored by Giuliano Grossi

Since Specialization
Citations

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

Fields of papers citing papers by Giuliano Grossi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Giuliano Grossi

This figure shows the co-authorship network connecting the top 25 collaborators of Giuliano Grossi. A scholar is included among the top collaborators of Giuliano Grossi 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 Giuliano Grossi. Giuliano Grossi 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.
2.
Boccignone, Giuseppe, Donatello Conte, Vittorio Cuculo, et al.. (2024). Enhancing rPPG pulse-signal recovery by facial sampling and PSD Clustering. Biomedical Signal Processing and Control. 101. 107158–107158. 3 indexed citations
3.
Islam, Md Shafiqul, et al.. (2023). Real-time face mask position recognition system based on MobileNet model. Smart Health. 28. 100382–100382. 12 indexed citations
4.
Conte, Donatello, Giuliano Grossi, Raffaella Lanzarotti, Jianyi Lin, & Alessandro Petrini. (2021). Analysis of a parallel MCMC algorithm for graph coloring with nearly uniform balancing. Pattern Recognition Letters. 149. 30–36. 3 indexed citations
5.
Petrini, Alessandro, Marco Mesiti, Max Schubach, et al.. (2020). parSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants. GigaScience. 9(5). 9 indexed citations
6.
Gliozzo, Jessica, Paolo Perlasca, Marco Mesiti, et al.. (2020). Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction. Scientific Reports. 10(1). 3612–3612. 7 indexed citations
7.
Cuculo, Vittorio, Alessandro D’Amelio, Giuliano Grossi, Raffaella Lanzarotti, & Jianyi Lin. (2019). Robust Single-Sample Face Recognition by Sparsity-Driven Sub-Dictionary Learning Using Deep Features. Sensors. 19(1). 146–146. 19 indexed citations
8.
Perlasca, Paolo, Marco Frasca, Marco Notaro, et al.. (2019). UNIPred-Web: a web tool for the integration and visualization of biomolecular networks for protein function prediction. BMC Bioinformatics. 20(1). 422–422. 6 indexed citations
9.
Boccignone, Giuseppe, et al.. (2018). Predictive Sampling of Facial Expression Dynamics Driven by a Latent Action Space. IRIS UNIMORE (University of Modena and Reggio Emilia). 143–150. 5 indexed citations
10.
Frasca, Marco, Giuliano Grossi, Jessica Gliozzo, et al.. (2018). A GPU-based algorithm for fast node label learning in large and unbalanced biomolecular networks. BMC Bioinformatics. 19(S10). 353–353. 1 indexed citations
11.
Grossi, Giuliano, et al.. (2017). Adaptation and validation of the "Continuing Bond Scale" in an Italian context. An instrument for studying the persistence of the bond with the deceased in normal and abnormal grief. SHILAP Revista de lepidopterología. 2 indexed citations
12.
Grossi, Giuliano, Raffaella Lanzarotti, & Jianyi Lin. (2017). Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation. PLoS ONE. 12(1). e0169663–e0169663. 9 indexed citations
13.
Grossi, Giuliano, et al.. (2016). Sparse decomposition by iterating Lipschitzian-type mappings. Theoretical Computer Science. 664. 12–28. 3 indexed citations
14.
Grossi, Giuliano, Raffaella Lanzarotti, & Jianyi Lin. (2016). Robust Face Recognition Providing the Identity and Its Reliability Degree Combining Sparse Representation and Multiple Features. International Journal of Pattern Recognition and Artificial Intelligence. 30(10). 1656007–1656007. 11 indexed citations
15.
Grossi, Giuliano, Raffaella Lanzarotti, & Jianyi Lin. (2015). High-rate compression of ECG signals by an accuracy-driven sparsity model relying on natural basis. Digital Signal Processing. 45. 96–106. 16 indexed citations
16.
Grossi, Giuliano & Federico Pedersini. (2010). Hub-betweenness analysis in delay tolerant networks inferred by real traces. HAL (Le Centre pour la Communication Scientifique Directe). 318–323.
17.
Grossi, Giuliano. (2009). ADAPTIVENESS IN MONOTONE PSEUDO-BOOLEAN OPTIMIZATION AND STOCHASTIC NEURAL COMPUTATION. International Journal of Neural Systems. 19(4). 241–252. 1 indexed citations
18.
Grossi, Giuliano, Massimo De Marchi, Enrico Pontelli, & Alessandro Provetti. (2008). Experimental Analysis of Graph-based Answer Set Computation over Parallel and Distributed Architectures. Journal of Logic and Computation. 19(4). 697–715. 2 indexed citations
19.
Bertoni, Alberto, et al.. (1997). Analysis of a Genetic Model.. 121–126. 3 indexed citations
20.
Grossi, Giuliano, et al.. (1995). Fast Prototyping for Hardware Neural Networks. 1. 411–416. 2 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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