Publication (by topic)
Optimization landscape, dynamical system, synchronization
On the landscape of synchronization networks: a perspective from nonconvex optimization. S. Ling, R. Xu, A. S. Bandeira, arXiv:1809.11083, Preprint, 2018. (Preprint)(arXiv)
Mathematics of data science
Certifying global optimality of graph cuts via semidefinite relaxation: a performance guarantee for spectral clustering. S. Ling, T. Strohmer, arXiv:1806.11429, Preprint, 2018. (Preprint)(arXiv)(Slides)
When do birds of a feather flock together? kmeans, proximity, and conic programming. X. Li, Y. Li, S. Ling, T. Strohmer, K. Wei, Mathematical Programming, Series A, 2018+. (Preprint)(Final)(Slides)
Nonconvex optimization and mathematics of signal processing
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, 2018+. (Preprint)(Final)(Slides)
Rapid, robust, and reliable blind deconvolution via nonconvex optimization. X. Li, S. Ling, T. Strohmer, K. Wei, Applied and Computational Harmonic Analysis, 2018+. (Preprint)(Final)(Slides)
Fast blind deconvolution and blind demixing via nonconvex optimization. S.Ling, T.Strohmer, International Conference on Sampling Theory and Applications (SampTA), pp.114118, 2017. (Final)
You can have it all – Fast algorithms for blind deconvolution, selfcalibration, and demixing. S.Ling, T.Strohmer, Mathematics in Imaging, MW1C.1, 2017. (Final)
Convex optimization and mathematics of signal processing
Selfcalibration and bilinear inverse problems via linear least squares. S. Ling, T. Strohmer, SIAM Journal on Imaging Sciences, Vol.11, No.1, pp.252292, 2018. (Preprint)(Final)
Blind deconvolution meets blind demixing: algorithms and performance bounds. S. Ling, T. Strohmer, IEEE Transactions on Information Theory, Vol.63, No.7, pp.4497  4520, July 2017. (Preprint)(Final)(Slides)
Simultaneous blind deconvolution and blind demixing via convex programming. S.Ling, T.Strohmer, 50th Asilomar Conference on Signals, Systems and Computers, pp.12231227, 2016. (Final)
Selfcalibration and biconvex compressive sensing. S. Ling, T. Strohmer, Inverse Problems, Vol. 31(11): 115002, 2015. (Preprint)(Final)(Slides)
(SIAM Student Paper Award 2017)
Numerical linear algebra
Backward error and perturbation bounds for high order Sylvester tensor equation. X. Shi, Y. Wei, S. Ling, Linear and Multilinear Algebra, 61 (10), 14361446, 2013. (Final)
Dissertation
Bilinear Inverse Problems: Theory, Algorithms, and Applications. S.Ling, University of California Davis, 2017, (Manucript)(Slides)
