Vanessa Gómez-Verdejo
- Signal Processing top 5%
- Blind Source Separation Techniques 10
- Speech and Audio Processing 3
- Artificial Intelligence top 5%
- Neural Networks and Applications 9
- Machine Learning and Data Classification 7
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- Face and Expression Recognition 15
- Cognitive Neuroscience top 10%
- Functional Brain Connectivity Studies 6
- Computational Mechanics top 10%
- Advanced Adaptive Filtering Techniques 3
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- Spectroscopy and Chemometric Analyses 5
- Co-authors
- Jerónimo Arenas‐GarcíaAnı́bal R. Figueiras-VidalManel Martínez‐RamónMichel VerleysenJussi TohkaEmilio Parrado-HernándezMiguel Lázaro-GredillaVince D. Calhoun
- Journals
- NeuroImage (1 paper)IEEE Transactions on Signal Processing (1 paper)PLoS Pathogens (1 paper)
- Partner nations
- SpainUnited StatesFinland
In The Last Decade
Vanessa Gómez-Verdejo
39 papers receiving 543 citations
Peers
Comparison fields: 5 of 91
- Signal Processing 133
- Artificial Intelligence 239
- Computer Vision and Pattern Recognition 122
- Cognitive Neuroscience 105
- Computational Mechanics 109
Countries citing papers authored by Vanessa Gómez-Verdejo
This map shows the geographic impact of Vanessa Gómez-Verdejo'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 Vanessa Gómez-Verdejo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vanessa Gómez-Verdejo more than expected).
Fields of papers citing papers by Vanessa Gómez-Verdejo
This network shows the impact of papers produced by Vanessa Gómez-Verdejo. 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 Vanessa Gómez-Verdejo. The network helps show where Vanessa Gómez-Verdejo may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Vanessa Gómez-Verdejo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2024 | 1 | |
| 3 | 2023 | 9 | |
| 4 | 2023 | 8 | |
| 5 | 2020 | 1 | |
| 6 | 2020 | 5 | |
| 7 | 2019 | 4 | |
| 8 | 2019 | 13 | |
| 9 | 2018 | 28 | |
| 10 | 2016 | 10 | |
| 11 | 2016 | 11 | |
| 12 | 2014 | 26 | |
| 13 | 2014 | 16 | |
| 14 | 2013 | 53 | |
| 15 | 2011 | 16 | |
| 16 | 2011 | 7 | |
| 17 | 2010 | 9 | |
| 18 | 2008 | 30 | |
| 19 | 2006 | 32 | |
| 20 | Boosting by weighting boundary and erroneous samples. | 2005 | 3 |
About Vanessa Gómez-Verdejo
Vanessa Gómez-Verdejo is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 41 papers that have together received 560 indexed citations. Recurring topics across this work include Face and Expression Recognition (15 papers), Blind Source Separation Techniques (10 papers), Neural Networks and Applications (9 papers), Machine Learning and Data Classification (7 papers), Functional Brain Connectivity Studies (6 papers), Spectroscopy and Chemometric Analyses (5 papers), Speech and Audio Processing (3 papers) and Advanced Adaptive Filtering Techniques (3 papers). The work is most often cited by research in Signal Processing (133 citations), Artificial Intelligence (239 citations) and Computer Vision and Pattern Recognition (122 citations). Vanessa Gómez-Verdejo has collaborated with scholars based in Spain, United States and Finland. Frequent co-authors include Jerónimo Arenas‐García, Anı́bal R. Figueiras-Vidal, Manel Martínez‐Ramón, Michel Verleysen, Jussi Tohka, Emilio Parrado-Hernández, Miguel Lázaro-Gredilla, Vince D. Calhoun, Eduardo Castro and Kent A. Kiehl. Their work appears in journals such as NeuroImage, IEEE Transactions on Signal Processing and PLoS Pathogens.
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.