Deanna Needell

11.2k total citations · 4 hit papers
112 papers, 6.5k citations indexed

About

Deanna Needell is a scholar working on Computational Mechanics, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Deanna Needell has authored 112 papers receiving a total of 6.5k indexed citations (citations by other indexed papers that have themselves been cited), including 72 papers in Computational Mechanics, 45 papers in Artificial Intelligence and 27 papers in Computer Vision and Pattern Recognition. Recurrent topics in Deanna Needell's work include Sparse and Compressive Sensing Techniques (70 papers), Stochastic Gradient Optimization Techniques (28 papers) and Blind Source Separation Techniques (19 papers). Deanna Needell is often cited by papers focused on Sparse and Compressive Sensing Techniques (70 papers), Stochastic Gradient Optimization Techniques (28 papers) and Blind Source Separation Techniques (19 papers). Deanna Needell collaborates with scholars based in United States, Canada and Israel. Deanna Needell's co-authors include Joel A. Tropp, Roman Vershynin, Yonina C. Eldar, Rachel Ward, Emmanuel J. Candès, Nathan Srebro, Aaditya Ramdas, Anna Ma, Mark A. Davenport and Michael B. Wakin and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Information Theory and Communications of the ACM.

In The Last Decade

Deanna Needell

98 papers receiving 6.1k citations

Hit Papers

CoSaMP: Iterative signal recovery from incomplete and ina... 2008 2026 2014 2020 2008 2008 2010 2010 500 1000 1.5k 2.0k 2.5k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
    2008 2.8k citations Deanna Needell, Joel A. Tropp Applied and Computational Harmonic Analysis profile →
  2. Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit
    2008 647 citations Deanna Needell et al. profile →
  3. Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit
    2010 567 citations Deanna Needell et al. profile →
  4. Compressed sensing with coherent and redundant dictionaries
    2010 532 citations Emmanuel J. Candès, Yonina C. Eldar et al. Applied and Computational Harmonic Analysis profile →

Peers

Deanna Needell
Roman Vershynin United States
Justin Romberg United States
Mark A. Davenport United States
Thomas Blumensath United Kingdom
Marco F. Duarte United States
Gitta Kutyniok Germany
Thomas Strohmer United States
Gonzalo R. Arce United States
Maryam Fazel United States
James H. McClellan United States
Roman Vershynin United States
Deanna Needell
Citations per year, relative to Deanna Needell Deanna Needell (= 1×) peers Roman Vershynin

Countries citing papers authored by Deanna Needell

Since Specialization
Citations

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

Fields of papers citing papers by Deanna Needell

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Deanna Needell

This figure shows the co-authorship network connecting the top 25 collaborators of Deanna Needell. A scholar is included among the top collaborators of Deanna Needell 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 Deanna Needell. Deanna Needell 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.
Dereziński, Michał, et al.. (2025). Randomized Kaczmarz Methods with Beyond-Krylov Convergence. SIAM Journal on Matrix Analysis and Applications. 46(4). 2558–2588.
2.
Needell, Deanna, et al.. (2024). Federated Gradient Matching Pursuit. IEEE Transactions on Information Theory. 70(6). 4512–4537.
3.
Hirn, Matthew, et al.. (2024). Geometric scattering on measure spaces. Applied and Computational Harmonic Analysis. 70. 101635–101635. 1 indexed citations
4.
Johnson, Lorraine, et al.. (2024). Optimizing Exclusion Criteria for Clinical Trials of Persistent Lyme Disease Using Real-World Data. Healthcare. 13(1). 20–20. 1 indexed citations
5.
Iwen, Mark, et al.. (2023). Modewise operators, the tensor restricted isometry property, and low-rank tensor recovery. Applied and Computational Harmonic Analysis. 66. 161–192.
6.
Liu, Feng, et al.. (2023). Continuous Semi-Supervised Nonnegative Matrix Factorization. Algorithms. 16(4). 187–187. 2 indexed citations
7.
Li, Pengyu, et al.. (2022). Guided Semi-Supervised Non-Negative Matrix Factorization. Algorithms. 15(5). 136–136. 5 indexed citations
8.
Needell, Deanna, et al.. (2021). Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositions. Journal of Machine Learning Research. 22(185). 1–36. 1 indexed citations
9.
Ma, Anna, You Zhou, Cynthia Rush, Dror Baron, & Deanna Needell. (2019). An Approximate Message Passing Framework for Side Information. IEEE Transactions on Signal Processing. 67(7). 1875–1888. 11 indexed citations
10.
Ravishankar, Saiprasad, Anna Ma, & Deanna Needell. (2019). Analysis of fast structured dictionary learning [Image: see text]. PubMed Central. 3 indexed citations
11.
Needell, Deanna, et al.. (2018). Simple Classification Using Binary Data. Journal of Machine Learning Research. 19(61). 1–30. 4 indexed citations
12.
Ma, Anna & Deanna Needell. (2017). Adapted Stochastic Gradient Descent for Linear Systems with Missing Data. arXiv (Cornell University). 2 indexed citations
13.
Needell, Deanna, et al.. (2017). Simple Object Classification Using Binary Data.. National Conference on Artificial Intelligence. 218–224. 1 indexed citations
14.
Böttcher, Albrecht, et al.. (2016). Lattices from equiangular tight frames. Linear Algebra and its Applications. 510. 395–420. 1 indexed citations
15.
Needell, Deanna, Nathan Srebro, & Rachel Ward. (2013). Stochastic gradient descent and the randomized Kaczmarz algorithm.. arXiv (Cornell University). 14 indexed citations
16.
Needell, Deanna & Joel A. Tropp. (2013). Paved with good intentions: Analysis of a randomized block Kaczmarz method. Linear Algebra and its Applications. 441. 199–221. 169 indexed citations
17.
Needell, Deanna & Rachel Ward. (2012). Total variation minimization for stable multidimensional signal recovery. arXiv (Cornell University). 5 indexed citations
18.
Candès, Emmanuel J., et al.. (2010). Compressed sensing with coherent and redundant dictionaries. Applied and Computational Harmonic Analysis. 31(1). 59–73. 532 indexed citations breakdown →
19.
Needell, Deanna. (2009). Noisy signal recovery via iterative reweighted L1-minimization. 113–117. 54 indexed citations
20.
Needell, Deanna & Joel A. Tropp. (2008). CoSaMP: Iterative signal recovery from incomplete and inaccurate samples. Applied and Computational Harmonic Analysis. 26(3). 301–321. 2807 indexed citations breakdown →

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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