Fabio Giampaolo
- Statistical and Nonlinear Physics top 1%
- Artificial Intelligence top 2%
- Computational Mechanics top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Mechanical Engineering top 10%
- Co-authors
- Francesco PiccialliSalvatore CuomoVincenzo Schiano Di ColaGianluigi RozzaMaziar RaissiGiancarlo FortinoVittorio Di SommaEdoardo Prezioso
- Topics
- Human Mobility and Location-Based Analysis (6 papers)Model Reduction and Neural Networks (6 papers)Privacy-Preserving Technologies in Data (6 papers)
- Partner nations
- ItalyChinaUnited States
In The Last Decade
Fabio Giampaolo
45 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 162
- Statistical and Nonlinear Physics 629
- Artificial Intelligence 583
- Computational Mechanics 242
- Radiology, Nuclear Medicine and Imaging 227
- Mechanical Engineering 206
Countries citing papers authored by Fabio Giampaolo
This map shows the geographic impact of Fabio Giampaolo'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 Fabio Giampaolo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fabio Giampaolo more than expected).
Fields of papers citing papers by Fabio Giampaolo
This network shows the impact of papers produced by Fabio Giampaolo. 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 Fabio Giampaolo. The network helps show where Fabio Giampaolo may publish in the future.
Co-authorship network of co-authors of Fabio Giampaolo
This figure shows the co-authorship network connecting the top 25 collaborators of Fabio Giampaolo. A scholar is included among the top collaborators of Fabio Giampaolo 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 Fabio Giampaolo. Fabio Giampaolo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 7 | |
| 6 | 7 | |
| 7 | 16 | |
| 8 | 38 | |
| 9 | 1 | |
| 10 | 1 | |
| 11 | 3 | |
| 12 | 1 | |
| 13 | 1 | |
| 14 | 17 | |
| 15 | 37 | |
| 16 | 48 | |
| 17 | 85 | |
| 18 | 235 | |
| 19 | 3 | |
| 20 | 46 |
About Fabio Giampaolo
Fabio Giampaolo is a scholar working on Transportation, Health Informatics and Artificial Intelligence, having authored 48 papers that have together received 2.1k indexed citations. Recurring topics across this work include Human Mobility and Location-Based Analysis (6 papers), Model Reduction and Neural Networks (6 papers) and Privacy-Preserving Technologies in Data (6 papers). The work is most often cited by research in Statistical and Nonlinear Physics (629 citations), Health Informatics (65 citations) and Artificial Intelligence (583 citations). Fabio Giampaolo has collaborated with scholars based in Italy, China and United States. Frequent co-authors include Francesco Piccialli, Salvatore Cuomo, Vincenzo Schiano Di Cola, Gianluigi Rozza, Maziar Raissi, Giancarlo Fortino, Vittorio Di Somma, Edoardo Prezioso, Diletta Chiaro and Stefano Izzo. Their work appears in journals such as Analytical Chemistry, Scientific Reports and Expert Systems with Applications.
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.