Stephen Becker
- Computational Mathematics top 2%
- Tensor decomposition and applications 5
- Acoustics and Ultrasonics top 5%
- Geophysics top 5%
- Geological and Geochemical Analysis 6
- Artificial Intelligence top 1%
- Stochastic Gradient Optimization Techniques 8
- Machine Learning and Algorithms 4
- Computational Mechanics top 2%
- Sparse and Compressive Sensing Techniques 22
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- Hydrocarbon exploration and reservoir analysis 7
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- Paleontology and Stratigraphy of Fossils 5
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- Blind Source Separation Techniques 4
- Co-authors
- Robert J. BodnarDavid GroßSteven T. FlammiaYi-Kai LiuJens EisertPeter EichhublYann LeCunVolkan Cevher
- Journals
- Geological Society of America Bulletin (3 papers)AAPG Bulletin (2 papers)Journal of Computational and Graphical Statistics (2 papers)
- Partner nations
- United StatesFranceSwitzerland
In The Last Decade
Stephen Becker
62 papers receiving 2.4k citations
Hit Papers
Peers
Comparison fields: 5 of 126
- Computational Mathematics 56
- Acoustics and Ultrasonics 38
- Geophysics 531
- Artificial Intelligence 967
- Computational Mechanics 594
Countries citing papers authored by Stephen Becker
This map shows the geographic impact of Stephen Becker'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 Stephen Becker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephen Becker more than expected).
Fields of papers citing papers by Stephen Becker
This network shows the impact of papers produced by Stephen Becker. 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 Stephen Becker. The network helps show where Stephen Becker may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Stephen Becker, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 7 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 4 | |
| 7 | 2024 | 2 | |
| 8 | 2023 | 2 | |
| 9 | 2023 | 0 | |
| 10 | 2022 | 70 | |
| 11 | 2020 | 1 | |
| 12 | 2020 | 9 | |
| 13 | Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch | 2018 | 37 |
| 14 | A Randomized Approach to Efficient Kernel Clustering | 2016 | 6 |
| 15 | QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models | 2014 | 3 |
| 16 | 2013 | 10 | |
| 17 | Quantum State Tomography via Compressed Sensingbreakdown → | 2010 | 652 |
| 18 | 2000 | 2 | |
| 19 | 1999 | 8 | |
| 20 | 1989 | 199 |
About Stephen Becker
Stephen Becker is a scholar working on Computational Mathematics, Computational Mechanics, Acoustics and Ultrasonics, Statistics and Probability and Structural Biology, having authored 70 papers that have together received 2.5k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (22 papers), Stochastic Gradient Optimization Techniques (8 papers), Hydrocarbon exploration and reservoir analysis (7 papers), Geological and Geochemical Analysis (6 papers), Tensor decomposition and applications (5 papers), Paleontology and Stratigraphy of Fossils (5 papers), Machine Learning and Algorithms (4 papers) and Blind Source Separation Techniques (4 papers). The work is most often cited by research in Computational Mathematics (56 citations), Acoustics and Ultrasonics (38 citations), Geophysics (531 citations), Artificial Intelligence (967 citations) and Computational Mechanics (594 citations). Stephen Becker has collaborated with scholars based in United States, France and Switzerland. Frequent co-authors include Robert J. Bodnar, David Groß, Steven T. Flammia, Yi-Kai Liu, Jens Eisert, Peter Eichhubl, Yann LeCun, Volkan Cevher, András Fall and Mark Schmidt. Their work appears in journals such as Geological Society of America Bulletin, AAPG Bulletin, Journal of Computational and Graphical Statistics, Physical Review Letters and Journal of Computational Physics.
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.