Mark Tygert

3.3k total citations
29 papers, 1.7k citations indexed

About

Mark Tygert is a scholar working on Computational Mechanics, Artificial Intelligence and Statistics and Probability. According to data from OpenAlex, Mark Tygert has authored 29 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computational Mechanics, 13 papers in Artificial Intelligence and 7 papers in Statistics and Probability. Recurrent topics in Mark Tygert's work include Sparse and Compressive Sensing Techniques (11 papers), Stochastic Gradient Optimization Techniques (8 papers) and Electromagnetic Scattering and Analysis (6 papers). Mark Tygert is often cited by papers focused on Sparse and Compressive Sensing Techniques (11 papers), Stochastic Gradient Optimization Techniques (8 papers) and Electromagnetic Scattering and Analysis (6 papers). Mark Tygert collaborates with scholars based in United States, Israel and Austria. Mark Tygert's 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 and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS ONE and Journal of Computational Physics.

In The Last Decade

Mark Tygert

29 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark Tygert United States 15 598 453 405 344 280 29 1.7k
Nathan Halko United States 4 860 1.4× 769 1.7× 377 0.9× 225 0.7× 501 1.8× 4 2.4k
Julien Langou United States 20 301 0.5× 249 0.5× 809 2.0× 274 0.8× 93 0.3× 55 2.1k
Sivasankaran Rajamanickam United States 21 371 0.6× 306 0.7× 434 1.1× 130 0.4× 481 1.7× 90 2.0k
Steven T. Smith United States 13 505 0.8× 458 1.0× 352 0.9× 130 0.4× 506 1.8× 33 2.6k
Bart Vandereycken Switzerland 17 573 1.0× 219 0.5× 315 0.8× 518 1.5× 218 0.8× 34 1.7k
Ren‐Cang Li United States 22 278 0.5× 144 0.3× 1.2k 3.1× 360 1.0× 115 0.4× 134 1.9k
James G. Nagy United States 26 905 1.5× 115 0.3× 368 0.9× 281 0.8× 1.1k 3.9× 107 2.5k
Ronald Cools Belgium 23 595 1.0× 294 0.6× 516 1.3× 304 0.9× 137 0.5× 116 2.1k
Maher Moakher Tunisia 21 480 0.8× 121 0.3× 307 0.8× 78 0.2× 302 1.1× 48 2.7k
Jeremy Du Croz United Kingdom 11 209 0.3× 287 0.6× 883 2.2× 225 0.7× 125 0.4× 16 2.4k

Countries citing papers authored by Mark Tygert

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Mark Tygert

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Tygert. A scholar is included among the top collaborators of Mark Tygert 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 Mark Tygert. Mark Tygert 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.
Lauter, Kristin, et al.. (2024). An Efficient Algorithm for Integer Lattice Reduction. SIAM Journal on Matrix Analysis and Applications. 45(1). 353–367. 1 indexed citations
2.
Tygert, Mark. (2023). Calibration of P-values for calibration and for deviation of a subpopulation from the full population. Advances in Computational Mathematics. 49(5). 1 indexed citations
3.
Tygert, Mark. (2021). A graphical method of cumulative differences between two subpopulations. Journal Of Big Data. 8(1). 1 indexed citations
4.
Guo, Chuan, et al.. (2020). Secure multiparty computations in floating-point arithmetic. Information and Inference A Journal of the IMA. 11(1). 103–135. 6 indexed citations
5.
Tygert, Mark, et al.. (2019). A hierarchical loss and its problems when classifying non-hierarchically. PLoS ONE. 14(12). e0226222–e0226222. 7 indexed citations
6.
Li, Huamin, Yuval Kluger, & Mark Tygert. (2018). Randomized algorithms for distributed computation of principal component analysis and singular value decomposition. Advances in Computational Mathematics. 44(5). 1651–1672. 5 indexed citations
7.
Li, Huamin, George C. Linderman, Arthur Szlam, et al.. (2017). Algorithm 971. ACM Transactions on Mathematical Software. 43(3). 1–14. 30 indexed citations
8.
Szlam, Arthur, Andrew Tulloch, & Mark Tygert. (2017). Accurate Low-Rank Approximations Via a Few Iterations of Alternating Least Squares. SIAM Journal on Matrix Analysis and Applications. 38(2). 425–433. 8 indexed citations
9.
Chintala, Soumith, Marc’Aurelio Ranzato, Arthur Szlam, et al.. (2016). Scale-invariant learning and convolutional networks. Applied and Computational Harmonic Analysis. 42(1). 154–166. 8 indexed citations
10.
Tygert, Mark, et al.. (2016). A Mathematical Motivation for Complex-Valued Convolutional Networks. Neural Computation. 28(5). 815–825. 62 indexed citations
11.
Rokhlin, Vladimir, et al.. (2011). A Fast Randomized Algorithm for Orthogonal Projection. SIAM Journal on Scientific Computing. 33(2). 849–868. 15 indexed citations
12.
Martinsson, Per‐Gunnar, Vladimir Rokhlin, & Mark Tygert. (2010). A randomized algorithm for the decomposition of matrices. Applied and Computational Harmonic Analysis. 30(1). 47–68. 193 indexed citations
13.
Tygert, Mark. (2010). Statistical tests for whether a given set of independent, identically distributed draws comes from a specified probability density. Proceedings of the National Academy of Sciences. 107(38). 16471–16476. 9 indexed citations
14.
Tygert, Mark. (2009). Recurrence relations and fast algorithms. Applied and Computational Harmonic Analysis. 28(1). 121–128. 12 indexed citations
15.
Rokhlin, Vladimir, Arthur Szlam, & Mark Tygert. (2009). A Randomized Algorithm for Principal Component Analysis. SIAM Journal on Matrix Analysis and Applications. 31(3). 1100–1124. 251 indexed citations
16.
Liberty, Edo, et al.. (2007). A fast randomized algorithm for the approximation of matrices. Applied and Computational Harmonic Analysis. 25(3). 335–366. 173 indexed citations
17.
Liberty, Edo, et al.. (2007). Randomized algorithms for the low-rank approximation of matrices. Proceedings of the National Academy of Sciences. 104(51). 20167–20172. 331 indexed citations
18.
Shkolnisky, Yoel, Mark Tygert, & Vladimir Rokhlin. (2006). Approximation of bandlimited functions. Applied and Computational Harmonic Analysis. 21(3). 413–420. 23 indexed citations
19.
Rokhlin, Vladimir & Mark Tygert. (2006). Fast Algorithms for Spherical Harmonic Expansions. SIAM Journal on Scientific Computing. 27(6). 1903–1928. 80 indexed citations
20.
Martinsson, Per‐Gunnar, Vladimir Rokhlin, & Mark Tygert. (2005). A fast algorithm for the inversion of general Toeplitz matrices. Computers & Mathematics with Applications. 50(5-6). 741–752. 47 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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