Cameron Musco
- Artificial Intelligence top 10%
- Computational Mechanics top 10%
- Computer Vision and Pattern Recognition top 10%
- Computational Theory and Mathematics top 10%
- Computer Networks and Communications
- Co-authors
- Christopher MuscoMichael B. CohenMichael KapralovYin Tat LeeAaron SidfordHaim AvronDavid P. WoodruffAmeya Velingker
- Topics
- Stochastic Gradient Optimization Techniques (11 papers)Sparse and Compressive Sensing Techniques (10 papers)Machine Learning and Algorithms (5 papers)
- Journals
- SIAM Journal on Matrix Analysis and ApplicationsTheory of ComputingCaltechAUTHORS (California Institute of Technology)
- Partner nations
- United StatesIsraelSwitzerland
In The Last Decade
Cameron Musco
25 papers receiving 271 citations
Peers
Comparison fields: 5 of 64
- Artificial Intelligence 176
- Computational Mechanics 85
- Computer Vision and Pattern Recognition 79
- Computational Theory and Mathematics 65
- Computer Networks and Communications 34
Countries citing papers authored by Cameron Musco
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 4 | |
| 4 | 3 | |
| 5 | Node Embeddings and Exact Low-Rank Representations of Complex Networks | 1 |
| 6 | Importance Sampling via Local Sensitivity | 1 |
| 7 | 6 | |
| 8 | 1 | |
| 9 | Learning to Prune: Speeding up Repeated Computations | 1 |
| 10 | 2 | |
| 11 | Inferring Networks From Random Walk-Based Node Similarities | 1 |
| 12 | Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? | 1 |
| 13 | 18 | |
| 14 | Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees | 15 |
| 15 | Recursive Sampling for the Nyström Method | 24 |
| 16 | 1 | |
| 17 | Faster eigenvector computation via shift-and-invert preconditioning | 2 |
| 18 | Stronger Approximate Singular Value Decomposition via the Block Lanczos and Power Methods. | 4 |
| 19 | Stronger and Faster Approximate Singular Value Decomposition via the Block Lanczos Method | 3 |
| 20 | 128 |
About Cameron Musco
Cameron Musco is a scholar working on Computational Mathematics, Artificial Intelligence and Computational Theory and Mathematics, having authored 27 papers that have together received 283 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (11 papers), Sparse and Compressive Sensing Techniques (10 papers) and Machine Learning and Algorithms (5 papers). The work is most often cited by research in Computational Mathematics (11 citations), Artificial Intelligence (176 citations) and Computational Mechanics (85 citations). Cameron Musco has collaborated with scholars based in United States, Israel and Switzerland. Frequent 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. Their work appears in journals such as SIAM Journal on Matrix Analysis and Applications, Theory of Computing and CaltechAUTHORS (California Institute of Technology).
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.