Davide Boscaini
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 2%
- Computational Mechanics top 2%
- Molecular Biology
- Computational Theory and Mathematics top 2%
- Co-authors
- Michael M. BronsteinEmanuele RodolàFederico MontiJonathan MasciJan SvobodaBruno E. CorreiaFreyr SverrissonPablo Gaínza
- Topics
- 3D Shape Modeling and Analysis (11 papers)Image Processing and 3D Reconstruction (4 papers)Advanced Neural Network Applications (3 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceNature MethodsMedical Image Analysis
- Partner nations
- ItalySwitzerlandUnited Kingdom
In The Last Decade
Davide Boscaini
18 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 126
- Computer Vision and Pattern Recognition 711
- Artificial Intelligence 562
- Computational Mechanics 456
- Molecular Biology 402
- Computational Theory and Mathematics 221
Countries citing papers authored by Davide Boscaini
This map shows the geographic impact of Davide Boscaini'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 Davide Boscaini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Davide Boscaini more than expected).
Fields of papers citing papers by Davide Boscaini
This network shows the impact of papers produced by Davide Boscaini. 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 Davide Boscaini. The network helps show where Davide Boscaini may publish in the future.
Co-authorship network of co-authors of Davide Boscaini
This figure shows the co-authorship network connecting the top 25 collaborators of Davide Boscaini. A scholar is included among the top collaborators of Davide Boscaini 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 Davide Boscaini. Davide Boscaini 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 | 3 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 5 | |
| 6 | 4 | |
| 7 | 0 | |
| 8 | 4 | |
| 9 | 58 | |
| 10 | 2 | |
| 11 | Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learningbreakdown → | 449 |
| 12 | 1 | |
| 13 | Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNsbreakdown → | 981 |
| 14 | 27 | |
| 15 | 81 | |
| 16 | 124 | |
| 17 | 30 | |
| 18 | 5 | |
| 19 | 2 | |
| 20 | 2 |
About Davide Boscaini
Davide Boscaini is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Geology, having authored 20 papers that have together received 1.8k indexed citations. Recurring topics across this work include 3D Shape Modeling and Analysis (11 papers), Image Processing and 3D Reconstruction (4 papers) and Advanced Neural Network Applications (3 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (164 citations), Computer Vision and Pattern Recognition (711 citations) and Geology (155 citations). Davide Boscaini has collaborated with scholars based in Italy, Switzerland and United Kingdom. Frequent co-authors include Michael M. Bronstein, Emanuele Rodolà, Federico Monti, Jonathan Masci, Jan Svoboda, Bruno E. Correia, Freyr Sverrisson, Pablo Gaínza, Fabio Poiesi and Umberto Castellani. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Nature Methods and Medical Image Analysis.
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.