Rayan Saab

1.6k total citations
43 papers, 855 citations indexed

About

Rayan Saab is a scholar working on Computational Mechanics, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Rayan Saab has authored 43 papers receiving a total of 855 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Computational Mechanics, 18 papers in Computer Vision and Pattern Recognition and 10 papers in Signal Processing. Recurrent topics in Rayan Saab's work include Sparse and Compressive Sensing Techniques (22 papers), Image and Signal Denoising Methods (13 papers) and Blind Source Separation Techniques (9 papers). Rayan Saab is often cited by papers focused on Sparse and Compressive Sensing Techniques (22 papers), Image and Signal Denoising Methods (13 papers) and Blind Source Separation Techniques (9 papers). Rayan Saab collaborates with scholars based in United States, Canada and China. Rayan Saab's co-authors include Özgür Yılmaz, Hassan Mansour, Michael P. Friedlander, Rachel Ward, Rick Chartrand, Felix J. Herrmann, Rafeef Abugharbieh, Martin J. McKeown, E. van den Berg and Gilles Hennenfent and has published in prestigious journals such as IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing and Geophysics.

In The Last Decade

Rayan Saab

40 papers receiving 796 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Rayan Saab United States 15 580 299 224 218 137 43 855
Christian Jutten France 6 578 1.0× 311 1.0× 363 1.6× 318 1.5× 132 1.0× 8 1.3k
Patrick Kuppinger Switzerland 4 763 1.3× 341 1.1× 266 1.2× 380 1.7× 266 1.9× 6 1.1k
Anamitra Makur Singapore 16 370 0.6× 165 0.6× 531 2.4× 331 1.5× 245 1.8× 98 1.1k
Badri Narayan Bhaskar United States 9 893 1.5× 369 1.2× 205 0.9× 671 3.1× 290 2.1× 12 1.5k
Karin Schnass Switzerland 10 621 1.1× 272 0.9× 256 1.1× 307 1.4× 132 1.0× 22 804
Jason T. Parker United States 11 659 1.1× 475 1.6× 85 0.4× 138 0.6× 262 1.9× 29 1.7k
S. Dimolitsas United States 8 213 0.4× 77 0.3× 370 1.7× 391 1.8× 146 1.1× 37 849
Nguyen T. Thao United States 11 173 0.3× 166 0.6× 346 1.5× 299 1.4× 135 1.0× 39 643
Xue Jiang China 22 326 0.6× 126 0.4× 246 1.1× 295 1.4× 199 1.5× 107 1.2k
İlker Bayram Türkiye 15 323 0.6× 154 0.5× 454 2.0× 228 1.0× 48 0.4× 39 986

Countries citing papers authored by Rayan Saab

Since Specialization
Citations

This map shows the geographic impact of Rayan Saab'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 Rayan Saab with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rayan Saab more than expected).

Fields of papers citing papers by Rayan Saab

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rayan Saab. 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 Rayan Saab. The network helps show where Rayan Saab may publish in the future.

Co-authorship network of co-authors of Rayan Saab

This figure shows the co-authorship network connecting the top 25 collaborators of Rayan Saab. A scholar is included among the top collaborators of Rayan Saab 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 Rayan Saab. Rayan Saab is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Saab, Rayan, et al.. (2025). Theoretical guarantees for low-rank compression of deep neural networks. Applied and Computational Harmonic Analysis. 82. 101837–101837.
2.
Saab, Rayan, et al.. (2023). A simple approach for quantizing neural networks. Applied and Computational Harmonic Analysis. 66. 138–150. 5 indexed citations
3.
Nicolas, Clara T., et al.. (2022). Coronavirus 2019 (COVID‐19) venovenous extracorporeal oxygenation: Single community hospital results and insights. Journal of Cardiac Surgery. 37(7). 2009–2014. 3 indexed citations
4.
Krahmer, Felix, et al.. (2019). Quantized compressed sensing for random circulant matrices. Applied and Computational Harmonic Analysis. 47(3). 1014–1032. 5 indexed citations
5.
Needell, Deanna, et al.. (2018). Simple Classification Using Binary Data. Journal of Machine Learning Research. 19(61). 1–30. 4 indexed citations
6.
Needell, Deanna, et al.. (2017). Simple Object Classification Using Binary Data.. National Conference on Artificial Intelligence. 218–224. 1 indexed citations
7.
Saab, Rayan, et al.. (2016). One-Bit Compressive Sensing With Norm Estimation. IEEE Transactions on Information Theory. 62(5). 2748–2758. 100 indexed citations
8.
Daubechies, Ingrid & Rayan Saab. (2015). A Deterministic Analysis of Decimation for Sigma-Delta Quantization of Bandlimited Functions. arXiv (Cornell University). 4 indexed citations
9.
Mansour, Hassan & Rayan Saab. (2015). Recovery analysis for weighted ℓ1-minimization using the null space property. Applied and Computational Harmonic Analysis. 43(1). 23–38. 31 indexed citations
10.
Iwen, Mark & Rayan Saab. (2013). Near-Optimal Encoding for Sigma-Delta Quantization of Finite Frame Expansions. Journal of Fourier Analysis and Applications. 19(6). 1255–1273. 4 indexed citations
11.
Krahmer, Felix, Rayan Saab, & Rachel Ward. (2012). Root-Exponential Accuracy for Coarse Quantization of Finite Frame Expansions. IEEE Transactions on Information Theory. 58(2). 1069–1079. 14 indexed citations
12.
Güntürk, C. Si̇nan, et al.. (2012). Sobolev Duals for Random Frames and ΣΔ Quantization of Compressed Sensing Measurements. Foundations of Computational Mathematics. 13(1). 1–36. 29 indexed citations
13.
Saab, Rayan & Özgür Yılmaz. (2009). Sparse recovery by non-convex optimization – instance optimality. Applied and Computational Harmonic Analysis. 29(1). 30–48. 79 indexed citations
14.
Saab, Rayan, et al.. (2008). Bayesian wavefield separation by transform-domain sparsity promotion. Geophysics. 73(5). A33–A38. 30 indexed citations
15.
Saab, Rayan, et al.. (2008). Curvelet-based primary-multiple separation from a Bayesian perspective. Open Collections. 15 indexed citations
16.
Saab, Rayan, Rick Chartrand, & Özgür Yılmaz. (2008). Stable sparse approximations via nonconvex optimization. Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. 3885–3888. 114 indexed citations
17.
Krishnamurthy, Vikram, Matthew Hoyles, Rayan Saab, & Shin‐Ho Chung. (2006). Permeation in Gramicidin Ion Channels by Directly Estimating the Potential of Mean Force Using Brownian Dynamics Simulations. Journal of Computational and Theoretical Nanoscience. 3(5). 702–711. 2 indexed citations
18.
McKeown, Martin J., et al.. (2006). Cortical muscle coupling in Parkinson’s disease (PD) bradykinesia. PubMed. 31–40. 13 indexed citations
19.
Saab, Rayan, Özgür Yılmaz, Martin J. McKeown, & Rafeef Abugharbieh. (2006). Blind Separation of Anechoic Under-determined Speech Mixtures using Multiple Sensors. 6. 642–646. 3 indexed citations
20.
Saab, Rayan, Martin J. McKeown, Lance Myers, & Rafeef Abugharbieh. (2005). A Wavelet Based Approach for the Detection of Coupling in EEG Signals. 616–620. 17 indexed citations

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.

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