Federico Monti
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Molecular Biology
- Computational Theory and Mathematics top 2%
- Computational Mechanics top 5%
- Co-authors
- Michael M. BronsteinEmanuele RodolàDavide BoscainiJonathan MasciJan SvobodaPablo GaínzaBruno E. CorreiaFreyr Sverrisson
- Topics
- Protein Structure and Dynamics (3 papers)Computational Drug Discovery Methods (3 papers)Video Surveillance and Tracking Methods (3 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignArtificial Intelligence
- Partner nations
- ItalySwitzerlandUnited Kingdom
In The Last Decade
Federico Monti
14 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 135
- Artificial Intelligence 688
- Computer Vision and Pattern Recognition 651
- Molecular Biology 444
- Computational Theory and Mathematics 269
- Computational Mechanics 230
Countries citing papers authored by Federico Monti
This map shows the geographic impact of Federico Monti'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 Federico Monti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Federico Monti more than expected).
Fields of papers citing papers by Federico Monti
This network shows the impact of papers produced by Federico Monti. 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 Federico Monti. The network helps show where Federico Monti may publish in the future.
Co-authorship network of co-authors of Federico Monti
This figure shows the co-authorship network connecting the top 25 collaborators of Federico Monti. A scholar is included among the top collaborators of Federico Monti 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 Federico Monti. Federico Monti 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 | 0 | |
| 3 | 6 | |
| 4 | 14 | |
| 5 | 60 | |
| 6 | Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learningbreakdown → | 449 |
| 7 | 1 | |
| 8 | 7 | |
| 9 | 32 | |
| 10 | Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks | 126 |
| 11 | 16 | |
| 12 | Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNsbreakdown → | 981 |
| 13 | 4 | |
| 14 | 148 | |
| 15 | 0 | |
| 16 | 5 |
About Federico Monti
Federico Monti is a scholar working on Biophysics, Transportation and Computer Vision and Pattern Recognition, having authored 16 papers that have together received 1.9k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (3 papers), Computational Drug Discovery Methods (3 papers) and Video Surveillance and Tracking Methods (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (651 citations), Computer Graphics and Computer-Aided Design (83 citations) and Artificial Intelligence (688 citations). Federico Monti has collaborated with scholars based in Italy, Switzerland and United Kingdom. Frequent co-authors include Michael M. Bronstein, Emanuele Rodolà, Davide Boscaini, Jonathan Masci, Jan Svoboda, Pablo Gaínza, Bruno E. Correia, Freyr Sverrisson, Xavier Bresson and Marco Tagliasacchi. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Communications and Nature Methods.
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.