Soledad Villar
- Artificial Intelligence
- Computer Vision and Pattern Recognition top 10%
- Molecular Biology
- Computational Theory and Mathematics
- Computational Mechanics
- Co-authors
- Afonso S. BandeiraDustin G. MixonJoan BrunaBianca DumitrascuBarbara E. EngelhardtRachel WardPranjal AwasthiMoses Charikar
- Topics
- Advanced Graph Neural Networks (4 papers)Sparse and Compressive Sensing Techniques (3 papers)Single-cell and spatial transcriptomics (2 papers)
- Journals
- Nature CommunicationsPhilosophical Transactions of the Royal Society A Mathematical Physical and Engineering SciencesTheoretical Computer Science
- Partner nations
- United StatesSlovakiaUnited Kingdom
In The Last Decade
Soledad Villar
13 papers receiving 172 citations
Peers
Comparison fields: 5 of 60
- Artificial Intelligence 73
- Computer Vision and Pattern Recognition 59
- Molecular Biology 44
- Computational Theory and Mathematics 21
- Computational Mechanics 18
Countries citing papers authored by Soledad Villar
This map shows the geographic impact of Soledad Villar'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 Soledad Villar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Soledad Villar more than expected).
Fields of papers citing papers by Soledad Villar
This network shows the impact of papers produced by Soledad Villar. 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 Soledad Villar. The network helps show where Soledad Villar may publish in the future.
Co-authorship network of co-authors of Soledad Villar
This figure shows the co-authorship network connecting the top 25 collaborators of Soledad Villar. A scholar is included among the top collaborators of Soledad Villar 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 Soledad Villar. Soledad Villar 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 | 1 | |
| 3 | 4 | |
| 4 | 7 | |
| 5 | 2 | |
| 6 | 43 | |
| 7 | 18 | |
| 8 | 1 | |
| 9 | 3 | |
| 10 | 10 | |
| 11 | 48 | |
| 12 | 10 | |
| 13 | 2 | |
| 14 | 33 |
About Soledad Villar
Soledad Villar is a scholar working on Biophysics, Computer Vision and Pattern Recognition and Computer Graphics and Computer-Aided Design, having authored 14 papers that have together received 182 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (4 papers), Sparse and Compressive Sensing Techniques (3 papers) and Single-cell and spatial transcriptomics (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (59 citations), Artificial Intelligence (73 citations) and Biophysics (10 citations). Soledad Villar has collaborated with scholars based in United States, Slovakia and United Kingdom. Frequent co-authors include Afonso S. Bandeira, Dustin G. Mixon, Joan Bruna, Bianca Dumitrascu, Barbara E. Engelhardt, Rachel Ward, Pranjal Awasthi, Moses Charikar, Ravishankar Krishnaswamy and Richard Kueng. Their work appears in journals such as Nature Communications, Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences and Theoretical Computer Science.
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.