Maria Girone

676 total citations
14 papers, 79 citations indexed

About

Maria Girone is a scholar working on Computer Networks and Communications, Nuclear and High Energy Physics and Artificial Intelligence. According to data from OpenAlex, Maria Girone has authored 14 papers receiving a total of 79 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Networks and Communications, 6 papers in Nuclear and High Energy Physics and 4 papers in Artificial Intelligence. Recurrent topics in Maria Girone's work include Distributed and Parallel Computing Systems (7 papers), Advanced Data Storage Technologies (7 papers) and Particle physics theoretical and experimental studies (5 papers). Maria Girone is often cited by papers focused on Distributed and Parallel Computing Systems (7 papers), Advanced Data Storage Technologies (7 papers) and Particle physics theoretical and experimental studies (5 papers). Maria Girone collaborates with scholars based in Switzerland, United States and Estonia. Maria Girone's co-authors include Matthias Neubert, Joosep Pata, X. Espinal Curull, S. Campana, J. Schovancova, G.J. McCance, Ian Bird, J. Duarte, Farouk Mokhtar and Panagiotis Spentzouris and has published in prestigious journals such as Physical Review Letters, SHILAP Revista de lepidopterología and Communications Physics.

In The Last Decade

Maria Girone

12 papers receiving 77 citations

Peers

Maria Girone

CNC

NHEP

ISM

HA

IS

M. Lamanna Switzerland

Kilian Schwarz Germany

A. Valassi Switzerland

V. Sapunenko Italy

T. Boccali Italy

S. Roiser Switzerland

F. Carena Switzerland

N. I. Geddes United Kingdom

W. Carena Switzerland

K. Cho South Korea

M. Lamanna Switzerland

Maria Girone

44 ×1.1

CNC

35 ×0.9

NHEP

16 ×0.9

ISM

10 ×0.8

HA

19 ×2.1

IS

Citations per year, relative to Maria Girone Maria Girone (= 1×) peers M. Lamanna

Countries citing papers authored by Maria Girone

Since Specialization

Citations

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

Fields of papers citing papers by Maria Girone

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maria Girone

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

All Works

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14 of 14 papers shown

1.

Amaya, Jorge, et al.. (2025). Energy efficiency trends in HPC: what high-energy and astrophysicists need to know. Frontiers in Physics. 13. 1 indexed citations

2.

Pata, Joosep, et al.. (2024). Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors. Communications Physics. 7(1). 4 indexed citations

3.

Riedel, Morris, et al.. (2024). Distributed hybrid quantum-classical performance prediction for hyperparameter optimization. Quantum Machine Intelligence. 6(2).

4.

Girone, Maria, et al.. (2024). Model Performance Prediction for Hyperparameter Optimization of Deep Learning Models Using High Performance Computing and Quantum Annealing. SHILAP Revista de lepidopterología. 295. 12005–12005. 1 indexed citations

5.

Girone, Maria, et al.. (2023). Hyperparameter optimization of data-driven AI models on HPC systems. Journal of Physics Conference Series. 2438(1). 12092–12092. 3 indexed citations

6.

Pata, Joosep, J. Duarte, Farouk Mokhtar, et al.. (2023). Machine Learning for Particle Flow Reconstruction at CMS. Journal of Physics Conference Series. 2438(1). 12100–12100. 9 indexed citations

7.

Barbet, Jean-Michel, Maria Girone, M. Javurkova, et al.. (2021). HEPiX Benchmarking Solution for WLCG Computing Resources. Repository KITopen (Karlsruhe Institute of Technology). 5(1). 4 indexed citations

8.

Meglio, Alberto Di, et al.. (2020). CERN openlab annual report 2019. Zenodo (CERN European Organization for Nuclear Research).

9.

Bird, Ian, S. Campana, Maria Girone, et al.. (2019). Architecture and prototype of a WLCG data lake for HL-LHC. SHILAP Revista de lepidopterología. 214. 4024–4024. 15 indexed citations

10.

Schovancova, J., et al.. (2018). Understanding the Performance of a Prototype of a WLCG Data Lake for HL-LHC. CERN Bulletin. 898. 332–333. 1 indexed citations

11.

Holzman, B., L. A. T. Bauerdick, Brian Bockelman, et al.. (2017). HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation. arXiv (Cornell University). 1(1). 16 indexed citations

12.

Bauer, D., D. Colling, Marc Dobson, et al.. (2015). The Diverse use of Clouds by CMS. Journal of Physics Conference Series. 664(2). 22012–22012. 6 indexed citations

13.

Girone, Maria & Matthias Neubert. (1996). Test of the Running ofαsinτDecays. Physical Review Letters. 76(17). 3061–3064. 18 indexed citations

14.

Girone, Maria. (1995). Global analysis of tau hadronic branching ratios in ALEPH. Nuclear Physics B - Proceedings Supplements. 40(1-3). 153–162. 1 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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