Rabab Ward

17.7k total citations · 4 hit papers
454 papers, 12.8k citations indexed

About

Rabab Ward is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Computational Mechanics. According to data from OpenAlex, Rabab Ward has authored 454 papers receiving a total of 12.8k indexed citations (citations by other indexed papers that have themselves been cited), including 218 papers in Computer Vision and Pattern Recognition, 118 papers in Signal Processing and 76 papers in Computational Mechanics. Recurrent topics in Rabab Ward's work include EEG and Brain-Computer Interfaces (75 papers), Sparse and Compressive Sensing Techniques (66 papers) and Advanced Image Processing Techniques (58 papers). Rabab Ward is often cited by papers focused on EEG and Brain-Computer Interfaces (75 papers), Sparse and Compressive Sensing Techniques (66 papers) and Advanced Image Processing Techniques (58 papers). Rabab Ward collaborates with scholars based in Canada, United States and China. Rabab Ward's co-authors include Angshul Majumdar, Z. Jane Wang, Gary E. Birch, Xun Chen, Mehrdad Fatourechi, Yü Liu, Ali Bashashati, Mohamed Elgendi, Qing Wang and Yongbo Liang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Genetics and SHILAP Revista de lepidopterología.

In The Last Decade

Rabab Ward

427 papers receiving 12.3k citations

Hit Papers

Image Fusion With Convolutional Sparse Repres... 2007 2026 2013 2019 2016 2007 2017 2019 200 400 600

Peers

Rabab Ward
Comparison fields: 5 of 196
  • Computer Vision and Pattern Recognition 4.8k
  • Cognitive Neuroscience 2.6k
  • Biomedical Engineering 2.5k
  • Media Technology 2.4k
  • Signal Processing 2.0k
Replace Xun Chen with:
Xun Chen China
Seong‐Whan Lee South Korea
Matti Pietikäinen Finland
Yü Liu China
Daoqiang Zhang China
Michael Cohen United States
J. M. Górriz Spain
Zhuowen Tu United States
Z. Jane Wang Canada
Frédo Durand United States
Xun Chen China View profile →
Citations per field, relative to Rabab Ward
Rabab Ward · 1×
Citations per year, relative to Rabab Ward
Rabab Ward · 1×

Countries citing papers authored by Rabab Ward

Since Specialization
Citations

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

Fields of papers citing papers by Rabab Ward

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rabab Ward

This figure shows the co-authorship network connecting the top 25 collaborators of Rabab Ward. A scholar is included among the top collaborators of Rabab Ward 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 Rabab Ward. Rabab Ward 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
# Work Indexed citations
1 0
2 1
3 4
4 0
5 7
6 39
7 13
8 18
9 36
10 92
11 28
12 8
13 80
14
Learning Input and Recurrent Weight Matrices in Echo State Networks
1
15 3
16 23
17 26
18 22
19 135
20
Obtaining the vocal-tract area function from the vowel sound
2

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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