Haim Avron

2.1k total citations
44 papers, 965 citations indexed

About

Haim Avron is a scholar working on Artificial Intelligence, Computational Mechanics and Computational Theory and Mathematics. According to data from OpenAlex, Haim Avron has authored 44 papers receiving a total of 965 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Artificial Intelligence, 20 papers in Computational Mechanics and 15 papers in Computational Theory and Mathematics. Recurrent topics in Haim Avron's work include Sparse and Compressive Sensing Techniques (18 papers), Stochastic Gradient Optimization Techniques (13 papers) and Matrix Theory and Algorithms (13 papers). Haim Avron is often cited by papers focused on Sparse and Compressive Sensing Techniques (18 papers), Stochastic Gradient Optimization Techniques (13 papers) and Matrix Theory and Algorithms (13 papers). Haim Avron collaborates with scholars based in Israel, United States and Switzerland. Haim Avron's co-authors include Sivan Toledo, Andrei Sharf, Chen Greif, Daniel Cohen‐Or, Vikas Sindhwani, Petar Maymounkov, David P. Woodruff, Lior Horesh, Christos Boutsidis and Anshul Gupta and has published in prestigious journals such as Proceedings of the National Academy of Sciences, IEEE Transactions on Pattern Analysis and Machine Intelligence and Technometrics.

In The Last Decade

Haim Avron

44 papers receiving 908 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Haim Avron Israel	16	380	377	264	211	90	44	965
Edo Liberty United States	18	432 1.1×	573 1.5×	269 1.0×	246 1.2×	171 1.9×	39	1.3k
Nicolas Boumal United States	17	495 1.3×	213 0.6×	228 0.9×	177 0.8×	67 0.7×	39	1.2k
Shivkumar Chandrasekaran United States	20	348 0.9×	195 0.5×	362 1.4×	633 3.0×	68 0.8×	51	1.5k
Afonso S. Bandeira United States	16	295 0.8×	252 0.7×	287 1.1×	120 0.6×	74 0.8×	44	1.0k
Raghu Meka United States	17	270 0.7×	332 0.9×	148 0.6×	305 1.4×	23 0.3×	48	792
Jonathan A. Kelner United States	21	172 0.5×	370 1.0×	311 1.2×	418 2.0×	116 1.3×	37	1.4k
Mark Rudelson United States	20	1.0k 2.7×	455 1.2×	290 1.1×	236 1.1×	64 0.7×	46	2.3k
Markus Hegland Australia	17	221 0.6×	108 0.3×	112 0.4×	199 0.9×	55 0.6×	103	991
Rachel Ward United States	19	811 2.1×	366 1.0×	339 1.3×	113 0.5×	113 1.3×	64	1.4k
James McKee United Kingdom	11	245 0.6×	233 0.6×	106 0.4×	463 2.2×	63 0.7×	32	1.2k

Countries citing papers authored by Haim Avron

Since Specialization

Citations

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

Fields of papers citing papers by Haim Avron

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Haim Avron

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

All Works

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20 of 20 papers shown

Ubaru, Shashanka, et al.. (2025). Multivariate Trace Estimation Using Quantum State Space Linear Algebra. SIAM Journal on Matrix Analysis and Applications. 46(1). 172–209. 1 indexed citations

Horesh, Lior, et al.. (2024). Stable tensor neural networks for efficient deep learning. Frontiers in Big Data. 7. 1363978–1363978. 1 indexed citations

Mor, Uria, et al.. (2023). Solving trust region subproblems using Riemannian optimization. Numerische Mathematik. 154(1-2). 1–33. 1 indexed citations

Avron, Haim, et al.. (2023). Experimental Design for Overparameterized Learning With Application to Single Shot Deep Active Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(10). 11766–11777. 2 indexed citations

Mor, Uria, Yotam Cohen, Rafael Valdés‐Mas, et al.. (2022). Dimensionality reduction of longitudinal ’omics data using modern tensor factorizations. PLoS Computational Biology. 18(7). e1010212–e1010212. 20 indexed citations

Ubaru, Shashanka, et al.. (2019). Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs. arXiv (Cornell University). 2 indexed citations

Horesh, Lior, et al.. (2018). Experimental Design for Nonparametric Correction of Misspecified Dynamical Models. SIAM/ASA Journal on Uncertainty Quantification. 6(2). 880–906. 7 indexed citations

Avron, Haim, et al.. (2017). Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees. International Conference on Machine Learning. 253–262. 15 indexed citations

Avron, Haim, Kenneth L. Clarkson, & David P. Woodruff. (2017). Faster Kernel Ridge Regression Using Sketching and Preconditioning. SIAM Journal on Matrix Analysis and Applications. 38(4). 1116–1138. 48 indexed citations

10.

Avron, Haim & Lior Horesh. (2015). Community Detection Using Time-Dependent Personalized PageRank. International Conference on Machine Learning. 1795–1803. 7 indexed citations

11.

Avron, Haim, Huy L. Nguyên, & David P. Woodruff. (2014). Subspace Embeddings for the Polynomial Kernel. Neural Information Processing Systems. 27. 2258–2266. 21 indexed citations

12.

Avron, Haim, Christos Boutsidis, Sivan Toledo, & Anastasios Zouzias. (2014). Efficient Dimensionality Reduction for Canonical Correlation Analysis. SIAM Journal on Scientific Computing. 36(5). S111–S131. 9 indexed citations

13.

Avron, Haim, et al.. (2013). A Randomized Asynchronous Linear Solver with Provable Convergence Rate. arXiv (Cornell University). 1 indexed citations

14.

Avron, Haim, Vikas Sindhwani, & David P. Woodruff. (2013). Sketching Structured Matrices for Faster Nonlinear Regression. Neural Information Processing Systems. 26. 2994–3002. 11 indexed citations

15.

Avron, Haim, et al.. (2013). Iterative Spectral Condition-Number Estimation. arXiv (Cornell University). 1 indexed citations

16.

Avron, Haim, et al.. (2013). Reliable Iterative Condition-Number Estimation. arXiv (Cornell University). 1 indexed citations

17.

Avron, Haim & Anshul Gupta. (2012). Managing data-movement for effective shared-memory parallelization of out-of-core sparse solvers. IEEE International Conference on High Performance Computing, Data, and Analytics. 1–11. 5 indexed citations

18.

Avron, Haim, Satyen Kale, Shiva Prasad Kasiviswanathan, & Vikas Sindhwani. (2012). Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization. arXiv (Cornell University). 323–330. 41 indexed citations

19.

Avron, Haim, Esmond Ng, & Sivan Toledo. (2009). Using Perturbed $QR$ Factorizations to Solve Linear Least-Squares Problems. SIAM Journal on Matrix Analysis and Applications. 31(2). 674–693. 15 indexed citations

20.

Avron, Haim, et al.. (2008). Parallel unsymmetric-pattern multifrontal sparse LU with column preordering. ACM Transactions on Mathematical Software. 34(2). 1–31. 8 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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