Laura Palagi

1.3k total citations
67 papers, 828 citations indexed

About

Laura Palagi is a scholar working on Numerical Analysis, Computational Theory and Mathematics and Computational Mechanics. According to data from OpenAlex, Laura Palagi has authored 67 papers receiving a total of 828 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Numerical Analysis, 19 papers in Computational Theory and Mathematics and 14 papers in Computational Mechanics. Recurrent topics in Laura Palagi's work include Advanced Optimization Algorithms Research (23 papers), Sparse and Compressive Sensing Techniques (13 papers) and Optimization and Variational Analysis (10 papers). Laura Palagi is often cited by papers focused on Advanced Optimization Algorithms Research (23 papers), Sparse and Compressive Sensing Techniques (13 papers) and Optimization and Variational Analysis (10 papers). Laura Palagi collaborates with scholars based in Italy, Austria and Sweden. Laura Palagi's co-authors include Stefano Lucidi, Marco Sciandrone, Enrico Sciubba, R. Lamedica, Alessandro Ruvio, Francisco Facchinei, Gianni Di Pillo, Massimo Roma, Apostolos Pesyridis and Marianna De Santis and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

Laura Palagi

62 papers receiving 778 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Laura Palagi Italy 16 193 166 138 106 106 67 828
Xiaohuan Li China 19 175 0.9× 242 1.5× 95 0.7× 32 0.3× 275 2.6× 101 1.1k
Dag Haugland Norway 15 115 0.6× 60 0.4× 46 0.3× 244 2.3× 107 1.0× 46 792
Mehmet Turan Söylemez Türkiye 15 37 0.2× 138 0.8× 42 0.3× 609 5.7× 98 0.9× 120 1.1k
Cristiano Cervellera Italy 15 57 0.3× 69 0.4× 177 1.3× 172 1.6× 102 1.0× 62 668
Shuqiang Huang China 14 42 0.2× 53 0.3× 105 0.8× 32 0.3× 200 1.9× 49 533
L.F.P. Etman Netherlands 20 121 0.6× 503 3.0× 59 0.4× 197 1.9× 154 1.5× 83 1.4k
Michaël Poss France 18 31 0.2× 69 0.4× 34 0.2× 211 2.0× 191 1.8× 69 982
Amir Ismail-Yahaya United States 7 22 0.1× 412 2.5× 77 0.6× 215 2.0× 80 0.8× 10 829
Connor Mattson United States 2 17 0.1× 239 1.4× 62 0.4× 162 1.5× 77 0.7× 5 569
Yuhui Zhang China 15 15 0.1× 155 0.9× 260 1.9× 26 0.2× 52 0.5× 68 723

Countries citing papers authored by Laura Palagi

Since Specialization
Citations

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

Fields of papers citing papers by Laura Palagi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laura Palagi

This figure shows the co-authorship network connecting the top 25 collaborators of Laura Palagi. A scholar is included among the top collaborators of Laura Palagi 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 Laura Palagi. Laura Palagi 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.
D’Agostino, Gregorio, et al.. (2025). OPT-RecSIM: An optimization-simulation integrated system for the repair sequence optimization problem. Electric Power Systems Research. 249. 111995–111995.
2.
Liuzzi, Giampaolo, et al.. (2025). Convergence of ease-controlled random reshuffling gradient algorithms under Lipschitz smoothness. Computational Optimization and Applications. 91(2). 933–971.
3.
Nastasi, Alberto, et al.. (2025). Multi-criteria Optimization Scheduling of Surgical Units: A Case Study at Policlinico Umberto I of Rome. Operations Research Forum. 6(2).
4.
Guarrasi, Valerio, Anne–Mieke Vandamme, Valeria Ghisetti, et al.. (2025). A graph neural network-based model with out-of-distribution robustness for enhancing antiretroviral therapy outcome prediction for HIV-1. Computerized Medical Imaging and Graphics. 120. 102484–102484. 7 indexed citations
5.
Amerini, Irene, et al.. (2024). Tuning parameters of deep neural network training algorithms pays off: a computational study. Top. 32(3). 579–620. 2 indexed citations
6.
Gentile, Claudio, et al.. (2024). Optimal Network Design for Municipal Waste Management: Application to the Metropolitan City of Rome. SHILAP Revista de lepidopterología. 8(3). 79–79. 1 indexed citations
7.
Incardona, Francesca, Ilaria Vicenti, Anders Sönnerborg, et al.. (2024). Incorporating temporal dynamics of mutations to enhance the prediction capability of antiretroviral therapy’s outcome for HIV-1. Bioinformatics. 40(6). 4 indexed citations
8.
Avella, Pasquale, et al.. (2023). A compact formulation for the base station deployment problem in wireless networks. Networks. 82(1). 52–67. 1 indexed citations
9.
Pietrabissa, Antonio, Stefano Battilotti, Claudia Califano, et al.. (2023). Deep Neural Network Regression to Assist Non-Invasive Diagnosis of Portal Hypertension. Healthcare. 11(18). 2603–2603. 2 indexed citations
10.
Palagi, Laura, et al.. (2023). Margin Optimal Classification Trees. SSRN Electronic Journal. 2 indexed citations
11.
Petti, Manuela, et al.. (2021). MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction. Genes. 12(11). 1713–1713. 5 indexed citations
12.
Mangone, Massimiliano, Francesco Agostini, Andrea Bernetti, et al.. (2021). Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis. PLoS ONE. 16(12). e0261511–e0261511. 11 indexed citations
13.
Romano, Silvia, Marco Salvetti, Andrea Tacchella, et al.. (2020). Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis. PLoS ONE. 15(3). e0230219–e0230219. 32 indexed citations
14.
Palagi, Laura, Massimiliano Mangone, Francesco Agostini, et al.. (2020). Data of patients undergoing rehabilitation programs. SHILAP Revista de lepidopterología. 30. 105419–105419. 31 indexed citations
15.
Palagi, Laura, et al.. (2019). The Sales Based Integer Program for Post-Departure Analysis in Airline Revenue Management: model and solution. RePEc: Research Papers in Economics. 1 indexed citations
16.
Facchinei, Francisco, et al.. (2014). User profile based Quality of Experience. IRIS Research product catalog (Sapienza University of Rome). 2 indexed citations
17.
Lucidi, Stefano, et al.. (2009). A Convergent Hybrid Decomposition Algorithm Model for SVM Training. IEEE Transactions on Neural Networks. 20(6). 1055–1060. 16 indexed citations
18.
Grippo, Luigi, Laura Palagi, & Veronica Piccialli. (2008). Necessary and sufficient global optimality conditions for NLP reformulations of linear SDP problems. Journal of Global Optimization. 44(3). 339–348. 4 indexed citations
19.
Palagi, Laura, et al.. (1985). The fast breeder reactor fuel cycle. Transactions of the American Nuclear Society. 48. 1 indexed citations
20.
Palagi, Laura, et al.. (1985). German results on consequences of LWR severe accidents. Transactions of the American Nuclear Society. 48. 41–59. 5 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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