Federico Galvanin

1.3k total citations
69 papers, 870 citations indexed

About

Federico Galvanin is a scholar working on Control and Systems Engineering, Biomedical Engineering and Materials Chemistry. According to data from OpenAlex, Federico Galvanin has authored 69 papers receiving a total of 870 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Control and Systems Engineering, 18 papers in Biomedical Engineering and 13 papers in Materials Chemistry. Recurrent topics in Federico Galvanin's work include Advanced Control Systems Optimization (17 papers), Innovative Microfluidic and Catalytic Techniques Innovation (13 papers) and Optimal Experimental Design Methods (11 papers). Federico Galvanin is often cited by papers focused on Advanced Control Systems Optimization (17 papers), Innovative Microfluidic and Catalytic Techniques Innovation (13 papers) and Optimal Experimental Design Methods (11 papers). Federico Galvanin collaborates with scholars based in United Kingdom, Italy and Switzerland. Federico Galvanin's co-authors include Fabrizio Bezzo, Massimiliano Barolo, Asterios Gavriilidis, Sandro Macchietto, Enhong Cao, Conor Waldron, Vivek Dua, Graham J. Hutchings, Stefano Cattaneo and Eric S. Fraga and has published in prestigious journals such as SHILAP Revista de lepidopterología, Advanced Functional Materials and Chemical Engineering Journal.

In The Last Decade

Federico Galvanin

62 papers receiving 843 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Federico Galvanin United Kingdom 17 282 266 211 165 121 69 870
Eric Bradford Norway 13 355 1.3× 302 1.1× 188 0.9× 188 1.1× 42 0.3× 20 912
Manuel Dahmen Germany 18 152 0.5× 315 1.2× 195 0.9× 118 0.7× 19 0.2× 46 942
Norbert Asprion Germany 20 673 2.4× 543 2.0× 111 0.5× 128 0.8× 46 0.4× 49 1.5k
Thomas A. Duever Canada 19 319 1.1× 97 0.4× 216 1.0× 65 0.4× 68 0.6× 85 1.2k
Guido Buzzi‐Ferraris Italy 15 222 0.8× 164 0.6× 155 0.7× 49 0.3× 45 0.4× 36 749
Tilman Barz Austria 19 255 0.9× 114 0.4× 87 0.4× 106 0.6× 74 0.6× 54 886
Pascal Floquet France 18 422 1.5× 200 0.8× 69 0.3× 129 0.8× 24 0.2× 64 799
Erik Esche Germany 18 266 0.9× 155 0.6× 188 0.9× 46 0.3× 23 0.2× 71 846
Conor M. McDonald United States 11 549 1.9× 315 1.2× 204 1.0× 68 0.4× 52 0.4× 14 968
Debasis Sarkar India 19 278 1.0× 197 0.7× 492 2.3× 95 0.6× 15 0.1× 54 1.0k

Countries citing papers authored by Federico Galvanin

Since Specialization
Citations

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

Fields of papers citing papers by Federico Galvanin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Federico Galvanin

This figure shows the co-authorship network connecting the top 25 collaborators of Federico Galvanin. A scholar is included among the top collaborators of Federico Galvanin 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 Federico Galvanin. Federico Galvanin 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.
Galvanin, Federico, et al.. (2025). Model-based design of experiments for adsorption isotherms. Adsorption. 31(8).
2.
Cattani, Federica, et al.. (2025). Foliar uptake of biocides: Statistical assessment of compartmental and diffusion-based models. Chemical Engineering Science. 317. 121984–121984. 1 indexed citations
3.
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Gao, Yaru, et al.. (2025). Predicting Aggregation Behavior of Nanoparticles in Liquid Crystals via Automated Data‐Driven Workflows. Advanced Functional Materials. 35(35). 1 indexed citations
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Petsagkourakis, Panagiotis, Muhammad Yusuf, Ricardo Labes, et al.. (2024). Automated kinetic model identification via cloud services using model-based design of experiments. Reaction Chemistry & Engineering. 9(7). 1859–1876. 8 indexed citations
8.
Bezzo, Fabrizio, et al.. (2024). An optimal experimental design framework for fast kinetic model identification based on artificial neural networks. Computers & Chemical Engineering. 187. 108752–108752. 4 indexed citations
9.
Cattani, Federica, et al.. (2024). Foliar Uptake Models for Biocides: Testing Practical Identifiability of Diffusion-Based Models. IFAC-PapersOnLine. 58(23). 73–78. 1 indexed citations
10.
Pal, Sayan, Maximilian O. Besenhard, Duncan Q.M. Craig, et al.. (2024). MLAPI: A framework for developing machine learning-guided drug particle syntheses in automated continuous flow platforms. Chemical Engineering Science. 302. 120780–120780. 2 indexed citations
11.
Gavriilidis, Asterios, et al.. (2023). Autonomous kinetic model identification using optimal experimental design and retrospective data analysis: methane complete oxidation as a case study. Reaction Chemistry & Engineering. 8(12). 3000–3017. 10 indexed citations
12.
Galvanin, Federico, et al.. (2023). Optimal design of infusion tests for the identification of physiological models of acquired von Willebrand syndrome. Chemical Engineering Science. 286. 119660–119660. 1 indexed citations
13.
Cattani, Federica, et al.. (2023). An optimal experimental design strategy for improving parameter estimation in stochastic models. Computers & Chemical Engineering. 170. 108133–108133. 1 indexed citations
14.
Waldron, Conor, et al.. (2019). A Multi-Objective Optimal Experimental Design Framework for Enhancing the Efficiency of Online Model Identification Platforms. Engineering. 5(6). 1049–1059. 11 indexed citations
15.
Waldron, Conor, et al.. (2019). Closed-Loop Model-Based Design of Experiments for Kinetic Model Discrimination and Parameter Estimation: Benzoic Acid Esterification on a Heterogeneous Catalyst. Industrial & Engineering Chemistry Research. 58(49). 22165–22177. 29 indexed citations
16.
Fraga, Eric S., et al.. (2017). A model-based data mining approach for determining the domain of validity of approximated models. Chemometrics and Intelligent Laboratory Systems. 172. 58–67. 7 indexed citations
17.
Galvanin, Federico, et al.. (2017). Formulation Screening and Freeze-Drying Process Optimization of Ginkgolide B Lyophilized Powder for Injection. AAPS PharmSciTech. 19(2). 541–550. 6 indexed citations
18.
Galvanin, Federico, et al.. (2015). Optimal design of experiments for the identification of kinetic models of methanol oxidation over silver catalyst. UCL Discovery (University College London). 9 indexed citations
19.
Galvanin, Federico, et al.. (2015). Optimal design of experiments for parameter identification in electrodialysis models. Process Safety and Environmental Protection. 105. 107–119. 29 indexed citations
20.
Galvanin, Federico, et al.. (2013). A general model-based design of experiments approach to achieve practical identifiability of pharmacokinetic and pharmacodynamic models. Journal of Pharmacokinetics and Pharmacodynamics. 40(4). 451–467. 32 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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