Andrea Loreggia

606 total citations
38 papers, 242 citations indexed

About

Andrea Loreggia is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, Andrea Loreggia has authored 38 papers receiving a total of 242 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Artificial Intelligence, 10 papers in Computer Vision and Pattern Recognition and 5 papers in Computational Theory and Mathematics. Recurrent topics in Andrea Loreggia's work include Logic, Reasoning, and Knowledge (6 papers), Semantic Web and Ontologies (6 papers) and Topic Modeling (5 papers). Andrea Loreggia is often cited by papers focused on Logic, Reasoning, and Knowledge (6 papers), Semantic Web and Ontologies (6 papers) and Topic Modeling (5 papers). Andrea Loreggia collaborates with scholars based in Italy, United States and Australia. Andrea Loreggia's co-authors include Francesca Rossi, Loris Nanni, Vijay Saraswat, Kristen Brent Venable, Nicholas Mattei, Yuri Malitsky, Horst Samulowitz, Alessandra Lumini, Sheryl Brahnam and Michele Donini and has published in prestigious journals such as SHILAP Revista de lepidopterología, Communications of the ACM and IEEE Access.

In The Last Decade

Andrea Loreggia

35 papers receiving 240 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Andrea Loreggia Italy 10 140 48 31 27 23 38 242
Negar Maleki United States 6 137 1.0× 33 0.7× 7 0.2× 14 0.5× 14 0.6× 9 304
Shurui Gui China 3 227 1.6× 75 1.6× 5 0.2× 31 1.1× 21 0.9× 3 357
Özgür Kafalı United Kingdom 10 108 0.8× 38 0.8× 10 0.3× 9 0.3× 36 1.6× 27 220
Yu-Liang Chou Taiwan 5 157 1.1× 18 0.4× 28 0.9× 2 0.1× 26 1.1× 16 253
Mohammad Al-Rubaie United States 4 226 1.6× 46 1.0× 20 0.6× 7 0.3× 42 1.8× 5 307
David Alvarez-Melis United States 5 300 2.1× 77 1.6× 12 0.4× 13 0.5× 6 0.3× 8 360
D. Ezhilmaran India 9 55 0.4× 39 0.8× 30 1.0× 16 0.6× 26 1.1× 46 197
Yushun Dong United States 12 349 2.5× 57 1.2× 134 4.3× 7 0.3× 21 0.9× 25 448
Andrew Trask United Kingdom 5 262 1.9× 38 0.8× 14 0.5× 6 0.2× 16 0.7× 6 354
Claude Turner United States 10 149 1.1× 36 0.8× 4 0.1× 9 0.3× 46 2.0× 23 264

Countries citing papers authored by Andrea Loreggia

Since Specialization
Citations

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

Fields of papers citing papers by Andrea Loreggia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andrea Loreggia

This figure shows the co-authorship network connecting the top 25 collaborators of Andrea Loreggia. A scholar is included among the top collaborators of Andrea Loreggia 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 Andrea Loreggia. Andrea Loreggia 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.
Loreggia, Andrea, et al.. (2025). The Summarization of Italian Tax-Law Decisions: The Case of the PRODIGIT Project. IEEE Access. 13. 38833–38855. 1 indexed citations
2.
Fabiano, Francesco, Andrea Loreggia, Nicholas Mattei, et al.. (2025). Thinking Fast and Slow in Human and Machine Intelligence. Communications of the ACM. 68(8). 72–79.
3.
Campbell, Murray, Francesco Fabiano, Lior Horesh, et al.. (2025). Fast, slow, and metacognitive thinking in AI. 1(1).
4.
Awad, Edmond, Sydney Levine, Andrea Loreggia, et al.. (2024). When is it acceptable to break the rules? Knowledge representation of moral judgements based on empirical data. Autonomous Agents and Multi-Agent Systems. 38(2). 3 indexed citations
5.
Nanni, Loris, Sheryl Brahnam, & Andrea Loreggia. (2024). An Enhanced Loss Function for Semantic Road Segmentation in Remote Sensing Images. IEEE Access. 12. 74218–74229. 1 indexed citations
6.
Nanni, Loris, et al.. (2023). A Standardized Approach for Skin Detection: Analysis of the Literature and Case Studies. Journal of Imaging. 9(2). 35–35. 7 indexed citations
7.
Nanni, Loris, Carlo Fantozzi, Andrea Loreggia, & Alessandra Lumini. (2023). Ensembles of Convolutional Neural Networks and Transformers for Polyp Segmentation. Sensors. 23(10). 4688–4688. 11 indexed citations
8.
Nanni, Loris, Sheryl Brahnam, Alessandra Lumini, & Andrea Loreggia. (2023). Coupling RetinaFace and Depth Information to Filter False Positives. Applied Sciences. 13(5). 2987–2987. 1 indexed citations
9.
Nanni, Loris, Andrea Loreggia, & Sheryl Brahnam. (2023). Comparison of Different Methods for Building Ensembles of Convolutional Neural Networks. Electronics. 12(21). 4428–4428. 3 indexed citations
10.
Nanni, Loris, et al.. (2023). Heterogeneous Ensemble for Medical Data Classification. SHILAP Revista de lepidopterología. 2(3). 676–693. 2 indexed citations
11.
Nanni, Loris, et al.. (2023). Varied Image Data Augmentation Methods for Building Ensemble. IEEE Access. 11. 8810–8823. 17 indexed citations
12.
Donini, Michele, et al.. (2021). Voting with random classifiers (VORACE): theoretical and experimental analysis. Autonomous Agents and Multi-Agent Systems. 35(2). 15 indexed citations
13.
Loreggia, Andrea, Nicholas Mattei, Francesca Rossi, & Kristen Brent Venable. (2019). Metric Learning for Value Alignment.. International Joint Conference on Artificial Intelligence. 1 indexed citations
14.
Rossi, Francesca & Andrea Loreggia. (2019). Preferences and Ethical Priorities: Thinking Fast and Slow in AI. Adaptive Agents and Multi-Agents Systems. 3–4. 8 indexed citations
15.
Loreggia, Andrea, Nicholas Mattei, Francesca Rossi, & Kristen Brent Venable. (2018). On the Distance Between CP-nets. Adaptive Agents and Multi-Agents Systems. 955–963. 5 indexed citations
16.
Loreggia, Andrea, Nicholas Mattei, Francesca Rossi, & Kristen Brent Venable. (2018). Preferences and Ethical Principles in Decision Making. Institutional Research Information System (Università degli Studi di Brescia). 222–222. 25 indexed citations
17.
Loreggia, Andrea, Francesca Rossi, & Kristen Brent Venable. (2017). Modelling Ethical Theories Compactly.. National Conference on Artificial Intelligence. 1 indexed citations
18.
Loreggia, Andrea, Nina Narodytska, Francesca Rossi, Kristen Brent Venable, & Toby Walsh. (2015). Controlling Elections by Replacing Candidates or Votes. Adaptive Agents and Multi-Agents Systems. 1737–1738. 6 indexed citations
19.
Grandi, Umberto, et al.. (2015). A Borda count for collective sentiment analysis. HAL (Le Centre pour la Communication Scientifique Directe). 2 indexed citations
20.
Grandi, Umberto, Andrea Loreggia, Francesca Rossi, & Vijay Saraswat. (2014). From Sentiment Analysis to Preference Aggregation.. Institutional Research Information System (Università degli Studi di Brescia). 4 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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