Cameron Musco

1.3k total citations
27 papers, 283 citations indexed

About

Cameron Musco is a scholar working on Artificial Intelligence, Computational Mechanics and Computational Theory and Mathematics. According to data from OpenAlex, Cameron Musco has authored 27 papers receiving a total of 283 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 11 papers in Computational Mechanics and 9 papers in Computational Theory and Mathematics. Recurrent topics in Cameron Musco's work include Stochastic Gradient Optimization Techniques (11 papers), Sparse and Compressive Sensing Techniques (10 papers) and Machine Learning and Algorithms (5 papers). Cameron Musco is often cited by papers focused on Stochastic Gradient Optimization Techniques (11 papers), Sparse and Compressive Sensing Techniques (10 papers) and Machine Learning and Algorithms (5 papers). Cameron Musco collaborates with scholars based in United States, Israel and Switzerland. Cameron Musco's co-authors include Christopher Musco, Michael B. Cohen, Michael Kapralov, Yin Tat Lee, Aaron Sidford, Haim Avron, David P. Woodruff, Ameya Velingker, Mohsen Ghaffari and Anne Greenbaum and has published in prestigious journals such as SIAM Journal on Matrix Analysis and Applications, Theory of Computing and CaltechAUTHORS (California Institute of Technology).

In The Last Decade

Cameron Musco

25 papers receiving 271 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Cameron Musco United States 8 176 85 79 65 34 27 283
Anastasios Zouzias Canada 9 211 1.2× 147 1.7× 106 1.3× 68 1.0× 50 1.5× 13 353
Thomas Bühler Germany 4 111 0.6× 54 0.6× 97 1.2× 79 1.2× 23 0.7× 4 293
Ali Çivril Türkiye 5 66 0.4× 81 1.0× 69 0.9× 51 0.8× 31 0.9× 6 239
Victor Bittorf United States 4 145 0.8× 70 0.8× 63 0.8× 24 0.4× 58 1.7× 5 254
Sushant Sachdeva United States 10 132 0.8× 33 0.4× 31 0.4× 154 2.4× 63 1.9× 32 307
Lorenzo Orecchia United States 11 141 0.8× 59 0.7× 38 0.5× 167 2.6× 109 3.2× 21 406
Kimon Fountoulakis United States 7 61 0.3× 72 0.8× 53 0.7× 33 0.5× 19 0.6× 13 197
Nigel Boston United States 14 135 0.8× 61 0.7× 93 1.2× 62 1.0× 46 1.4× 75 535
Matus Telgarsky United States 7 223 1.3× 121 1.4× 54 0.7× 43 0.7× 25 0.7× 15 469
Yiqiao Zhong United States 7 79 0.4× 81 1.0× 24 0.3× 12 0.2× 18 0.5× 14 295

Countries citing papers authored by Cameron Musco

Since Specialization
Citations

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

Fields of papers citing papers by Cameron Musco

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Cameron Musco

This figure shows the co-authorship network connecting the top 25 collaborators of Cameron Musco. A scholar is included among the top collaborators of Cameron Musco 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 Cameron Musco. Cameron Musco 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.
2.
Freire, Juliana, et al.. (2023). Weighted Minwise Hashing Beats Linear Sketching for Inner Product Estimation. 169–181. 2 indexed citations
3.
Musco, Cameron, Christopher Musco, David P. Woodruff, & Taisuke Yasuda. (2022). Active Linear Regression for ℓp Norms and Beyond. 38. 744–753. 4 indexed citations
4.
McCallum, Andrew, et al.. (2022). Sublinear Time Approximation of Text Similarity Matrices. Proceedings of the AAAI Conference on Artificial Intelligence. 36(7). 8072–8080. 3 indexed citations
5.
Musco, Cameron, et al.. (2020). Node Embeddings and Exact Low-Rank Representations of Complex Networks. Neural Information Processing Systems. 33. 13185–13198. 1 indexed citations
6.
Musco, Cameron, et al.. (2020). Importance Sampling via Local Sensitivity. International Conference on Artificial Intelligence and Statistics. 3099–3109. 1 indexed citations
7.
Cohen, Michael B., Cameron Musco, & Jakub Pachocki. (2020). . Theory of Computing. 16(1). 1–25. 6 indexed citations
8.
Lynch, Nancy, et al.. (2020). Random Sketching, Clustering, and Short-Term Memory in Spiking Neural Networks.. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 31. 1 indexed citations
9.
Kalai, Adam Tauman, et al.. (2019). Learning to Prune: Speeding up Repeated Computations. CaltechAUTHORS (California Institute of Technology). 30–33. 1 indexed citations
10.
Mallmann-Trenn, Frederik, Cameron Musco, & Christopher Musco. (2018). Eigenvector Computation and Community Detection in Asynchronous Gossip\n Models. arXiv (Cornell University). 2 indexed citations
11.
Hoskins, Jeremy G., Cameron Musco, Christopher Musco, & Charalampos E. Tsourakakis. (2018). Inferring Networks From Random Walk-Based Node Similarities. neural information processing systems. 31. 3704–3715. 1 indexed citations
12.
Musco, Cameron & David P. Woodruff. (2017). Is Input Sparsity Time Possible for Kernel Low-Rank Approximation?. Neural Information Processing Systems. 30. 4435–4445. 1 indexed citations
13.
Cohen, Michael B., Cameron Musco, & Christopher Musco. (2017). Input sparsity time low-rank approximation via ridge leverage score sampling. Symposium on Discrete Algorithms. 1758–1777. 18 indexed citations
14.
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
15.
Musco, Cameron & Christopher Musco. (2017). Recursive Sampling for the Nyström Method. arXiv (Cornell University). 30. 3834–3846. 24 indexed citations
16.
Lynch, Nancy, Cameron Musco, & Merav Parter. (2017). Computational Tradeoffs in Biological Neural Networks: Self-Stabilizing Winner-Take-All Networks. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 1 indexed citations
17.
Garber, Dan, Elad Hazan, Chi Jin, et al.. (2016). Faster eigenvector computation via shift-and-invert preconditioning. 2626–2634. 2 indexed citations
18.
Musco, Cameron & Christopher Musco. (2015). Stronger Approximate Singular Value Decomposition via the Block Lanczos and Power Methods.. arXiv (Cornell University). 4 indexed citations
19.
Musco, Cameron & Christopher Musco. (2015). Stronger and Faster Approximate Singular Value Decomposition via the Block Lanczos Method. arXiv (Cornell University). 3 indexed citations
20.
Cohen, Michael B., et al.. (2015). Dimensionality Reduction for k-Means Clustering and Low Rank Approximation. 163–172. 128 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Anastasios Zouzias Breakdown of academic impact, for papers by Thomas Bühler Breakdown of academic impact, for papers by Ali Çivril Breakdown of academic impact, for papers by Victor Bittorf Breakdown of academic impact, for papers by Sushant Sachdeva Breakdown of academic impact, for papers by Lorenzo Orecchia Breakdown of academic impact, for papers by Kimon Fountoulakis Breakdown of academic impact, for papers by Nigel Boston Breakdown of academic impact, for papers by Matus Telgarsky Breakdown of academic impact, for papers by Yiqiao Zhong
Rankless by CCL
2026