Nico Vervliet
- Computational Mathematics top 0.5%
- Tensor decomposition and applications 23
- Signal Processing top 10%
- Blind Source Separation Techniques 5
- Computational Mechanics top 10%
- Sparse and Compressive Sensing Techniques 7
- Hardware and Architecture top 10%
- Parallel Computing and Optimization Techniques 4
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- Advanced Neuroimaging Techniques and Applications 5
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- Matrix Theory and Algorithms 6
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- Model Reduction and Neural Networks 4
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- Computational Physics and Python Applications 3
- Co-authors
- Lieven De LathauwerOtto DebalsLaurent SorberNele MoelansNicolas GillisXiao FuKejun HuangIgnat Domanov
- Journals
- IEEE Transactions on Signal Processing (2 papers)IEEE Signal Processing Magazine (2 papers)SIAM Journal on Scientific Computing (1 paper)
- Partner nations
- BelgiumUnited StatesChina
In The Last Decade
Nico Vervliet
22 papers receiving 358 citations
Peers
Comparison fields: 5 of 53
- Computational Mathematics 278
- Signal Processing 100
- Computational Mechanics 125
- Hardware and Architecture 30
- Radiology, Nuclear Medicine and Imaging 74
Countries citing papers authored by Nico Vervliet
This map shows the geographic impact of Nico Vervliet'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 Nico Vervliet with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nico Vervliet more than expected).
Fields of papers citing papers by Nico Vervliet
This network shows the impact of papers produced by Nico Vervliet. 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 Nico Vervliet. The network helps show where Nico Vervliet may publish in the future.
Co-authorship network
The 14 scholars most cited alongside Nico Vervliet, 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 | 2024 | 1 | |
| 2 | 2024 | 0 | |
| 3 | 2022 | 2 | |
| 4 | 2022 | 1 | |
| 5 | 2020 | 1 | |
| 6 | 2020 | 19 | |
| 7 | 2019 | 1 | |
| 8 | 2019 | 19 | |
| 9 | 2019 | 1 | |
| 10 | 2019 | 5 | |
| 11 | 2018 | 0 | |
| 12 | 2018 | 17 | |
| 13 | 2017 | 6 | |
| 14 | 2017 | 13 | |
| 15 | 2016 | 4 | |
| 16 | 2016 | 60 | |
| 17 | 2015 | 52 | |
| 18 | Structured data fusion using Tensorlab : a demonstration | 2014 | 3 |
| 19 | Breaking the Curse of Dimensionality using Decompositions of Incomplete Tensors | 2014 | 4 |
| 20 | 2014 | 117 |
About Nico Vervliet
Nico Vervliet is a scholar working on Computational Mathematics, Structural Biology and Hardware and Architecture, having authored 27 papers that have together received 372 indexed citations. Recurring topics across this work include Tensor decomposition and applications (23 papers), Sparse and Compressive Sensing Techniques (7 papers), Matrix Theory and Algorithms (6 papers), Advanced Neuroimaging Techniques and Applications (5 papers), Blind Source Separation Techniques (5 papers), Parallel Computing and Optimization Techniques (4 papers), Model Reduction and Neural Networks (4 papers) and Computational Physics and Python Applications (3 papers). The work is most often cited by research in Computational Mathematics (278 citations), Signal Processing (100 citations) and Computational Mechanics (125 citations). Nico Vervliet has collaborated with scholars based in Belgium, United States and China. Frequent co-authors include Lieven De Lathauwer, Otto Debals, Laurent Sorber, Nele Moelans, Nicolas Gillis, Xiao Fu, Kejun Huang, Ignat Domanov, Sabine Van Huffel and Marc Van Barel. Their work appears in journals such as IEEE Transactions on Signal Processing, IEEE Signal Processing Magazine and SIAM Journal on Scientific Computing.
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.