| Self-calibration and biconvex compressive sensing S Ling, T Strohmer Inverse Problems 31 (11), 115002, 2015 | 179 | 2015 |
| Rapid, robust, and reliable blind deconvolution via nonconvex optimization X Li, S Ling, T Strohmer, K Wei Applied and Computational Harmonic Analysis 47 (3), 893-934, 2019 | 171 | 2019 |
| Blind deconvolution meets blind demixing: algorithms and performance bounds S Ling, T Strohmer IEEE Transactions on Information Theory 63 (7), 4497-4520, 2017 | 90 | 2017 |
| Self-calibration and bilinear inverse problems via linear least squares S Ling, T Strohmer SIAM Journal on Imaging Sciences 11 (1), 252-292, 2018 | 42* | 2018 |
| Regularized gradient descent: a nonconvex recipe for fast joint blind deconvolution and demixing S Ling, T Strohmer Information and Inference: A Journal of the IMA 8 (1), 1-49, 2019 | 36 | 2019 |
| When do birds of a feather flock together? k-means, proximity, and conic programming X Li, Y Li, S Ling, T Strohmer, K Wei Mathematical Programming, Series A 179 (1), 295-341, 2020 | 21 | 2020 |
| Backward error and perturbation bounds for high order Sylvester tensor equation X Shi, Y Wei, S Ling Linear and Multilinear Algebra 61 (10), 1436-1446, 2013 | 21 | 2013 |
| On the landscape of synchronization networks: a perspective from nonconvex optimization S Ling, R Xu, AS Bandeira SIAM Journal on Optimization 29 (3), 1879-1907, 2019 | 17 | 2019 |
| Certifying global optimality of graph cuts via semidefinite relaxation: a performance guarantee for spectral clustering S Ling, T Strohmer Foundations of Computational Mathematics 20 (3), 368-421, 2020 | 10 | 2020 |
| Near-optimal performance bounds for orthogonal and permutation group synchronization via spectral methods S Ling arXiv preprint arXiv:2008.05341, 2020 | 4 | 2020 |
| Solving orthogonal group synchronization via convex and low-rank optimization: tightness and landscape analysis S Ling arXiv preprint arXiv:2006.00902, 2020 | 4 | 2020 |
| Strong consistency, graph Laplacians, and the stochastic block model S Deng, S Ling, T Strohmer arXiv preprint arXiv:2004.09780, 2020 | 3 | 2020 |
| Fast blind deconvolution and blind demixing via nonconvex optimization S Ling, T Strohmer 2017 International Conference on Sampling Theory and Applications (SampTA …, 2017 | 1 | 2017 |
| Simultaneous blind deconvolution and blind demixing via convex programming S Ling, T Strohmer 2016 50th Asilomar Conference on Signals, Systems and Computers, 1223-1227, 2016 | 1 | 2016 |
| Improved performance guarantees for orthogonal group synchronization via generalized power method S Ling arXiv preprint arXiv:2012.00470, 2020 | | 2020 |
| On the critical coupling of the finite Kuramoto model on dense networks S Ling arXiv preprint arXiv:2004.03202, 2020 | | 2020 |
| DS-GA 3001 Special Topics in Data Science: Mathematics of Data Science: Graphs and Networks (Spring 2018) AS Bandeira, S Ling | | 2018 |
| You can have it all -- Fast algorithms for blind deconvolution, self-calibration, and demixing S Ling, T Strohmer Mathematics in Imaging, MW1C.1, 2017 | | 2017 |
| Bilinear inverse problems: theory, algorithms, and applications S Ling University of California, Davis, 2017 | | 2017 |
Thomas StrohmerProfessor of Mathematics, University of California at DavisDirección de correo verificada de zoho.com
