Giuseppe Cuccu
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Automotive Engineering
- Control and Systems Engineering
- Computational Theory and Mathematics
- Co-authors
- Faustino GomezJürgen SchmidhuberJan KoutníkPhilippe Cudré-MaurouxLeonardo VanneschiTobias GlasmachersMatthew LuciwLjiljana Dolamic
- Topics
- Reinforcement Learning in Robotics (8 papers)Evolutionary Algorithms and Applications (7 papers)Metaheuristic Optimization Algorithms Research (5 papers)
- Journals
- Autonomous Agents and Multi-Agent SystemsBOA (University of Milano-Bicocca)Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences)
- Partner nations
- SwitzerlandUnited StatesGermany
In The Last Decade
Giuseppe Cuccu
14 papers receiving 151 citations
Peers
Comparison fields: 5 of 51
- Artificial Intelligence 118
- Computer Vision and Pattern Recognition 35
- Automotive Engineering 29
- Control and Systems Engineering 20
- Computational Theory and Mathematics 17
Countries citing papers authored by Giuseppe Cuccu
This map shows the geographic impact of Giuseppe Cuccu'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 Giuseppe Cuccu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Giuseppe Cuccu more than expected).
Fields of papers citing papers by Giuseppe Cuccu
This network shows the impact of papers produced by Giuseppe Cuccu. 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 Giuseppe Cuccu. The network helps show where Giuseppe Cuccu may publish in the future.
Co-authorship network of co-authors of Giuseppe Cuccu
This figure shows the co-authorship network connecting the top 25 collaborators of Giuseppe Cuccu. A scholar is included among the top collaborators of Giuseppe Cuccu 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 Giuseppe Cuccu. Giuseppe Cuccu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 4 | |
| 3 | 24 | |
| 4 | 2 | |
| 5 | 0 | |
| 6 | 2 | |
| 7 | 13 | |
| 8 | 8 | |
| 9 | 66 | |
| 10 | 11 | |
| 11 | 8 | |
| 12 | 12 | |
| 13 | 3 | |
| 14 | 6 | |
| 15 | 5 |
About Giuseppe Cuccu
Giuseppe Cuccu is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Statistics, Probability and Uncertainty, having authored 15 papers that have together received 165 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (8 papers), Evolutionary Algorithms and Applications (7 papers) and Metaheuristic Optimization Algorithms Research (5 papers). The work is most often cited by research in Artificial Intelligence (118 citations), Automotive Engineering (29 citations) and Computer Vision and Pattern Recognition (35 citations). Giuseppe Cuccu has collaborated with scholars based in Switzerland, United States and Germany. Frequent co-authors include Faustino Gomez, Jürgen Schmidhuber, Jan Koutník, Philippe Cudré-Mauroux, Leonardo Vanneschi, Tobias Glasmachers, Matthew Luciw, Ljiljana Dolamic, Jie Yang and Julian Togelius. Their work appears in journals such as Autonomous Agents and Multi-Agent Systems, BOA (University of Milano-Bicocca) and Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences).
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.