Marco Lorusso
Impact in
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- Particle Detector Development and Performance
- Neutrino Physics Research
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- Parallel Computing and Optimization Techniques
Papers in
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- Computational Physics and Python Applications 3
- Anomaly Detection Techniques and Applications 1
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- Particle Detector Development and Performance 3
- Particle physics theoretical and experimental studies 1
- Neutrino Physics Research 1
- Co-authors
- T. Diotalevi (1 shared paper)R. Travaglini (3 shared papers)C. Battilana (1 shared paper)D. Bonacorsi (4 shared papers)M. Dūma (1 shared paper)Davide Salomoni (2 shared papers)
- Journals
- Proceedings of 41st International Conference on High Energy physics — PoS(ICHEP2022) (1 paper)Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (2 papers)CERN Document Server (European Organization for Nuclear Research) (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 6 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 | 2022 | 0 |
About Marco Lorusso
Marco Lorusso is a scholar working on Artificial Intelligence, Nuclear and High Energy Physics, Hardware and Architecture, Information Systems and Management and Infectious Diseases, having authored 4 papers that have together received 6 indexed citations. Recurring topics across this work include Computational Physics and Python Applications (3 papers), Particle Detector Development and Performance (3 papers), Parallel Computing and Optimization Techniques (2 papers), Particle physics theoretical and experimental studies (1 paper), Neutrino Physics Research (1 paper), Big Data Technologies and Applications (1 paper) and Anomaly Detection Techniques 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 T. Diotalevi, R. Travaglini, C. Battilana, D. Bonacorsi, M. Dūma and Davide Salomoni. Their work appears in journals such as Proceedings of 41st International Conference on High Energy physics — PoS(ICHEP2022), Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) and CERN Document Server (European Organization for Nuclear Research).
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.