Mark Tygert
Impact in
- Computational Mathematics top 0.5%
- Tensor decomposition and applications
- Computational Mechanics top 2%
- Sparse and Compressive Sensing Techniques
Papers in
-
- Sparse and Compressive Sensing Techniques 11
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- Stochastic Gradient Optimization Techniques 8
- Neural Networks and Applications 3
- Co-authors
- Vladimir Rokhlin (10 shared papers)Per‐Gunnar Martinsson (5 shared papers)Edo Liberty (2 shared papers)Arthur Szlam (5 shared papers)Yoel Shkolnisky (2 shared papers)Nathan Halko (1 shared paper)Yann LeCun (2 shared papers)Soumith Chintala (2 shared papers)
- Journals
- Applied and Computational Harmonic Analysis (6 papers)Proceedings of the National Academy of Sciences (3 papers)SIAM Journal on Scientific Computing (3 papers)SIAM Journal on Matrix Analysis and Applications (3 papers)Journal of Computational Physics (2 papers)
- Partner nations
- United StatesIsraelAustria
In The Last Decade
Mark Tygert
29 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 139
- Computational Mathematics 150
- Computational Mechanics 607
- Computational Theory and Mathematics 410
- Artificial Intelligence 461
- Computer Vision and Pattern Recognition 285
Countries citing papers authored by Mark Tygert
This map shows the geographic impact of Mark Tygert'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 Mark Tygert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Tygert more than expected).
Fields of papers citing papers by Mark Tygert
This network shows the impact of papers produced by Mark Tygert. 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 Mark Tygert. The network helps show where Mark Tygert may publish in the future.
Co-authors
The 25 scholars most cited alongside Mark Tygert, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2007 | 337 | |
| 2 | 2009 | 255 | |
| 3 | 2010 | 199 | |
| 4 | 2007 | 178 | |
| 5 | 2011 | 172 | |
| 6 | 2008 | 107 | |
| 7 | 2006 | 81 | |
| 8 | 2016 | 64 | |
| 9 | 2005 | 49 | |
| 10 | 2008 | 46 | |
| 11 | 2010 | 45 | |
| 12 | 2017 | 32 | |
| 13 | 2006 | 24 | |
| 14 | 2006 | 17 | |
| 15 | 2011 | 15 | |
| 16 | 2011 | 12 | |
| 17 | 2009 | 12 | |
| 18 | 2010 | 9 | |
| 19 | 2019 | 8 | |
| 20 | 2016 | 8 |
About Mark Tygert
Mark Tygert is a scholar working on Computational Mechanics, Artificial Intelligence, Statistics and Probability, Computer Vision and Pattern Recognition and Atomic and Molecular Physics, and Optics, having authored 29 papers that have together received 1.7k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (11 papers), Stochastic Gradient Optimization Techniques (8 papers), Electromagnetic Scattering and Analysis (6 papers), Advanced Statistical Methods and Models (4 papers), Tensor decomposition and applications (3 papers), Statistical Methods and Bayesian Inference (3 papers), Statistical Methods and Inference (3 papers) and Neural Networks and Applications (3 papers). The work is most often cited by research in Computational Mathematics (150 citations), Computational Mechanics (607 citations), Computational Theory and Mathematics (410 citations), Artificial Intelligence (461 citations) and Computer Vision and Pattern Recognition (285 citations). Mark Tygert has collaborated with scholars based in United States, Israel and Austria. Frequent co-authors include Vladimir Rokhlin, Per‐Gunnar Martinsson, Edo Liberty, Arthur Szlam, Yoel Shkolnisky, Nathan Halko, Yann LeCun, Soumith Chintala, Joan Bruna and Huamin Li. Their work appears in journals such as Applied and Computational Harmonic Analysis, Proceedings of the National Academy of Sciences, SIAM Journal on Scientific Computing, SIAM Journal on Matrix Analysis and Applications 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.