Akram Aldroubi
- Computational Mathematics top 0.5%
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- Image and Signal Denoising Methods 67
- Medical Image Segmentation Techniques 10
- Applied Mathematics top 0.2%
- Mathematical Analysis and Transform Methods 47
- Signal Processing top 0.2%
- Digital Filter Design and Implementation 20
- Blind Source Separation Techniques 12
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- Medical Imaging Techniques and Applications 13
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- Sparse and Compressive Sensing Techniques 20
- Advanced Numerical Analysis Techniques 16
- Co-authors
- Michaël UnserM. EdenPeter J. BasserSinisa PajevicCarlo PierpaoliJeffrey DudaKarlheinz GröchenigMurray Eden
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (3 papers)NeuroImage (1 paper)Proceedings of the IEEE (1 paper)
- Partner nations
- United StatesFranceArgentina
In The Last Decade
Akram Aldroubi
128 papers receiving 9.2k citations
Hit Papers
Peers
Comparison fields: 5 of 160
- Computational Mathematics 258
- Computer Vision and Pattern Recognition 4.5k
- Applied Mathematics 2.0k
- Signal Processing 1.7k
- Radiology, Nuclear Medicine and Imaging 3.3k
Countries citing papers authored by Akram Aldroubi
This map shows the geographic impact of Akram Aldroubi'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 Akram Aldroubi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akram Aldroubi more than expected).
Fields of papers citing papers by Akram Aldroubi
This network shows the impact of papers produced by Akram Aldroubi. 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 Akram Aldroubi. The network helps show where Akram Aldroubi may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Akram Aldroubi, 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 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2023 | 3 | |
| 4 | 2022 | 5 | |
| 5 | 2018 | 6 | |
| 6 | 2017 | 10 | |
| 7 | 2016 | 34 | |
| 8 | 2013 | 3 | |
| 9 | 2011 | 31 | |
| 10 | Optimal non-linear models | 2009 | 2 |
| 11 | 2005 | 15 | |
| 12 | 2002 | 100 | |
| 13 | 2002 | 6 | |
| 14 | Non-Uniform Sampling In Shift-Invariant Spaces | 2000 | 1 |
| 15 | In vivo fiber tractography using DT-MRI databreakdown → | 2000 | 2281 |
| 16 | Wavelets, multiwavelets, and their applications : AMS Special Session on Wavelets, Multiwavelets, and Their Applications, January, 1997, San Diego, California | 1998 | 1 |
| 17 | 1995 | 16 | |
| 18 | 1995 | 75 | |
| 19 | 1994 | 84 | |
| 20 | 1992 | 23 |
About Akram Aldroubi
Akram Aldroubi is a scholar working on Computational Mathematics, Applied Mathematics and Computer Vision and Pattern Recognition, having authored 134 papers that have together received 9.8k indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (67 papers), Mathematical Analysis and Transform Methods (47 papers), Sparse and Compressive Sensing Techniques (20 papers), Digital Filter Design and Implementation (20 papers), Advanced Numerical Analysis Techniques (16 papers), Medical Imaging Techniques and Applications (13 papers), Blind Source Separation Techniques (12 papers) and Medical Image Segmentation Techniques (10 papers). The work is most often cited by research in Computational Mathematics (258 citations), Computer Vision and Pattern Recognition (4.5k citations) and Applied Mathematics (2.0k citations). Akram Aldroubi has collaborated with scholars based in United States, France and Argentina. Frequent co-authors include Michaël Unser, M. Eden, Peter J. Basser, Sinisa Pajevic, Carlo Pierpaoli, Jeffrey Duda, Karlheinz Gröchenig, Murray Eden, Gustavo K. Rohde and Benoît M. Dawant. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, NeuroImage and Proceedings of the IEEE.
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.