S. Giagu

138.2k total citations
24 papers, 67 citations indexed

About

S. Giagu is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, S. Giagu has authored 24 papers receiving a total of 67 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Artificial Intelligence and 6 papers in Pulmonary and Respiratory Medicine. Recurrent topics in S. Giagu's work include Particle Detector Development and Performance (6 papers), Particle physics theoretical and experimental studies (6 papers) and Medical Imaging Techniques and Applications (5 papers). S. Giagu is often cited by papers focused on Particle Detector Development and Performance (6 papers), Particle physics theoretical and experimental studies (6 papers) and Medical Imaging Techniques and Applications (5 papers). S. Giagu collaborates with scholars based in Italy, Switzerland and Australia. S. Giagu's co-authors include A. Messina, C. Voena, A. Coccaro, L. Sabetta, Luca Torresi, C. Mancini-Terracciano, Christian Napoli, Simone Scardapane, M. Bauce and G. Lamanna and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment.

In The Last Decade

S. Giagu

21 papers receiving 64 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
S. Giagu Italy	5	27	17	11	8	7	24	67
M. Pettee United States	5	58 2.1×	33 1.9×	8 0.7×	9 1.1×	5 0.7×	6	101
Sergei Gleyzer United States	6	62 2.3×	29 1.7×	12 1.1×	3 0.4×	10 1.4×	26	95
G. Quétant Switzerland	4	46 1.7×	17 1.0×	6 0.5×	11 1.4×	5 0.7×	7	71
M. Hushchyn Russia	5	14 0.5×	12 0.7×	9 0.8×	5 0.6×	3 0.4×	15	50
F. Ratnikov Russia	6	61 2.3×	18 1.1×	4 0.4×	5 0.6×	6 0.9×	28	90
G. Schott Netherlands	3	61 2.3×	14 0.8×	8 0.7×	2 0.3×	3 0.4×	4	80
F. Psihas United States	3	80 3.0×	18 1.1×	8 0.7×	5 0.6×	6 0.9×	4	108
A. Glazov Russia	6	61 2.3×	15 0.9×	2 0.2×	4 0.5×	7 1.0×	16	98
A. Radovic Austria	2	75 2.8×	17 1.0×	8 0.7×	5 0.6×	5 0.7×	4	101
G. Pawloski United States	2	72 2.7×	17 1.0×	7 0.6×	5 0.6×	5 0.7×	4	98

Countries citing papers authored by S. Giagu

Since Specialization

Citations

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

Fields of papers citing papers by S. Giagu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S. Giagu

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Bolst, David, Matthew D. Cameron, Stéphanie Corde, et al.. (2025). Comparison of Deep Learning Models for fast and accurate dose map prediction in Microbeam Radiation Therapy. Physica Medica. 136. 105012–105012.

Gargiulo, Simona, et al.. (2025). Influence based explainability of brain tumors segmentation in magnetic resonance imaging. Progress in Artificial Intelligence. 2 indexed citations

Wood, Lincoln C., et al.. (2025). Mixture-of-experts graph transformers for interpretable particle collision detection. Scientific Reports. 15(1). 27906–27906.

Giagu, S., et al.. (2025). Quantum noise modeling through reinforcement learning. Quantum Science and Technology. 11(1). 15005–15005. 1 indexed citations

Franciosini, Gaia, et al.. (2024). Fast and precise dose estimation for very high energy electron radiotherapy with graph neural networks. Frontiers in Physics. 12. 1 indexed citations

Voena, C., et al.. (2024). Integrating ChatGPT-4: A Novel XAI Interface for Enhanced Clinician Understanding of MRI Image Segmentation Results. 320–325. 1 indexed citations

Dilaghi, Emanuele, C. Voena, S. Giagu, et al.. (2024). A pre-training model for intestinal metaplasia recognition in the gastric corpus: preliminary data analysis. Endoscopy. 56(S 02). S58–S58. 1 indexed citations

Fumero, Giuseppe, et al.. (2024). Retrieving genuine nonlinear Raman responses in ultrafast spectroscopy via deep learning. APL Photonics. 9(6). 1 indexed citations

Nicoletti, Lorenzo, et al.. (2023). Convergent Approaches to AI Explainability for HEP Muonic Particles Pattern Recognition. IRIS Research product catalog (Sapienza University of Rome). 7(1). 2 indexed citations

10.

Coccaro, A., et al.. (2023). Fast neural network inference on FPGAs for triggering on long-lived particles at colliders. Machine Learning Science and Technology. 4(4). 45040–45040. 5 indexed citations

11.

Giagu, S., et al.. (2023). Long-Lived Particles Anomaly Detection with Parametrized Quantum Circuits. SHILAP Revista de lepidopterología. 6(1). 297–311. 7 indexed citations

12.

Giagu, S., et al.. (2023). Convolutional neural network based decoders for surface codes. Quantum Information Processing. 22(3). 5 indexed citations

13.

Giagu, S., et al.. (2023). Nearest Neighbours Graph Variational AutoEncoder. Algorithms. 16(3). 143–143. 2 indexed citations

14.

Giagu, S., et al.. (2023). Artificial neural networks exploiting point cloud data for fragmented solid objects classification. Machine Learning Science and Technology. 4(4). 45025–45025. 3 indexed citations

15.

Giagu, S., et al.. (2022). Tau Lepton Identification With Graph Neural Networks at Future Electron–Positron Colliders. Frontiers in Physics. 10. 3 indexed citations

16.

Francescato, S., et al.. (2021). Model compression and simplification pipelines for fast deep neural network inference in FPGAs in HEP. The European Physical Journal C. 81(11). 7 indexed citations

17.

Asai, M., G.A.P. Cirrone, M. Colonna, et al.. (2020). Preliminary results in using Deep Learning to emulate BLOB, a nuclear interaction model. Physica Medica. 73. 65–72. 2 indexed citations

18.

Giagu, S.. (2019). WIMP Dark Matter Searches With the ATLAS Detector at the LHC. Frontiers in Physics. 7. 13 indexed citations

19.

Ammendola, Roberto, M. Bauce, A. Biagioni, et al.. (2015). Graphics Processing Units for HEP trigger systems. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 824. 307–310. 1 indexed citations

20.

Ammendola, Roberto, M. Bauce, A. Biagioni, et al.. (2013). The GAP project - GPU for realtime applications in high energy physics and medical imaging. CINECA IRIS Institutial research information system (University of Pisa). 396. 1–7.

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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