Marco Lorusso
Impact in
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- Particle Detector Development and Performance
- Astrophysics and Cosmic Phenomena
- Neutrino Physics Research
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- Parallel Computing and Optimization Techniques
Papers in
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- Particle Detector Development and Performance 4
- Particle physics theoretical and experimental studies 2
- Neutrino Physics Research 1
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- Parallel Computing and Optimization Techniques 2
- Co-authors
- C. Battilana (1 shared paper)Riccardo Travaglini (3 shared papers)D. Bonacorsi (4 shared papers)T. Diotalevi (1 shared paper)M. Dūma (1 shared paper)Simone Gasperini (1 shared paper)G. Bianco (1 shared paper)Davide Salomoni (2 shared papers)
- Journals
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (2 papers)CERN Document Server (European Organization for Nuclear Research) (1 paper)Proceedings of 41st International Conference on High Energy physics — PoS(ICHEP2022) (1 paper)
- Partner nations
- Italy
In The Last Decade
Marco Lorusso
3 papers receiving 6 citations
Peers
Comparison fields: 5 of 5
- Nuclear and High Energy Physics 4
- Hardware and Architecture 2
- Artificial Intelligence 4
- Computer Vision and Pattern Recognition 2
- Computer Networks and Communications 1
Countries citing papers authored by Marco Lorusso
This map shows the geographic impact of Marco Lorusso'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 Marco Lorusso with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco Lorusso more than expected).
Fields of papers citing papers by Marco Lorusso
This network shows the impact of papers produced by Marco Lorusso. 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 Marco Lorusso. The network helps show where Marco Lorusso may publish in the future.
Co-authors
The 9 scholars most cited alongside Marco Lorusso, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 3 | |
| 2 | 2023 | 2 | |
| 3 | 2022 | 1 | |
| 4 | 2025 | 0 | |
| 5 | 2022 | 0 |
About Marco Lorusso
Marco Lorusso is a scholar working on Nuclear and High Energy Physics, Hardware and Architecture, Artificial Intelligence, Information Systems and Management and Computer Networks and Communications, having authored 5 papers that have together received 6 indexed citations. Recurring topics across this work include Particle Detector Development and Performance (4 papers), Computational Physics and Python Applications (3 papers), Particle physics theoretical and experimental studies (2 papers), Parallel Computing and Optimization Techniques (2 papers), Distributed and Parallel Computing Systems (1 paper), Neutrino Physics Research (1 paper), Anomaly Detection Techniques and Applications (1 paper) and Big Data Technologies and Applications (1 paper). The work is most often cited by research in Nuclear and High Energy Physics (4 citations), Hardware and Architecture (2 citations), Artificial Intelligence (4 citations), Computer Vision and Pattern Recognition (2 citations) and Computer Networks and Communications (1 citation). Marco Lorusso has collaborated with scholars based in Italy. Frequent co-authors include C. Battilana, Riccardo Travaglini, D. Bonacorsi, T. Diotalevi, M. Dūma, Simone Gasperini, G. Bianco, Davide Salomoni and M. Franchini. Their work appears in journals such as Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna), CERN Document Server (European Organization for Nuclear Research) and Proceedings of 41st International Conference on High Energy physics — PoS(ICHEP2022).
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.