Rachel Ward

60 papers receiving 1.3k citations

Peers

Rachel Ward
Comparison fields: 5 of 109
  • Computational Mathematics 26
  • Computational Mechanics 811
  • Acoustics and Ultrasonics 19
  • Signal Processing 194
  • Computer Vision and Pattern Recognition 339
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Citations per year

Countries citing papers authored by Rachel Ward

Since Specialization
Citations

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

Fields of papers citing papers by Rachel Ward

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Rachel Ward, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Rachel Ward Line = papers co-authored together Rachel Ward links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20240
2 20241
3 20234
4 20227
5 20220
6
Function Approximation via Sparse Random Features.
20212
7 20208
8
AdaGrad stepsizes: Sharp convergence over nonconvex landscapes, from any initialization
201824
9
AdaGrad stepsizes: Sharp convergence over nonconvex landscapes
201828
10
The Local Convexity of Solving Quadratic Equations
20155
11 201518
12 20154
13
Coherent Matrix Completion
201441
14
Coherent Matrix Completion
20136
15
Stochastic gradient descent and the randomized Kaczmarz algorithm.
201314
16
Total variation minimization for stable multidimensional signal recovery
20125
17
Compressive imaging: stable and robust recovery from variable density frequency samples
20124
18 2012112
19 2011143
20
Cross Validation in Compressed Sensing via the Johnson Lindenstrauss Lemma
20084

About Rachel Ward

Rachel Ward is a scholar working on Computational Mathematics, Computational Mechanics, Statistics and Probability, Mathematical Physics and Signal Processing, having authored 64 papers that have together received 1.4k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (34 papers), Stochastic Gradient Optimization Techniques (11 papers), Microwave Imaging and Scattering Analysis (11 papers), Blind Source Separation Techniques (8 papers), Numerical methods in inverse problems (7 papers), Medical Imaging Techniques and Applications (5 papers), Image and Signal Denoising Methods (5 papers) and Model Reduction and Neural Networks (5 papers). The work is most often cited by research in Computational Mathematics (26 citations), Computational Mechanics (811 citations), Acoustics and Ultrasonics (19 citations), Signal Processing (194 citations) and Computer Vision and Pattern Recognition (339 citations). Rachel Ward has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Deanna Needell, Holger Rauhut, Nathan Srebro, Massimo Fornasier, Rayan Saab, Felix Krahmer, Sujay Sanghavi, Xiaoxia Wu, Léon Bottou and Hayden Schaeffer. Their work appears in journals such as IEEE Transactions on Information Theory, Applied and Computational Harmonic Analysis, Foundations of Computational Mathematics, Journal of Approximation Theory and SIAM Journal on Mathematical Analysis.

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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