Shuyang Ling

Shuyang Ling is an Assistant Professor Faculty Fellow of Data Science at NYU Shanghai. Prior to joining NYU Shanghai, he was a Courant Instructor/Assistant Professor at the Courant Institute of Mathematics and Center for Data Science, New York University, from 2017-2019.

Ling's research focuses broadly on the mathematics of data science. He is interested in tackling inverse problems from engineering applications and extracting meaningful information from large-scale and heterogeneous datasets. His research involves a broad spectrum of subjects including optimization, probability, statistics, computational harmonic analysis, and numerical linear algebra. 

Select Publications

• Shuyang Ling and Thomas Strohmer. Self-calibration and biconvex compressive sensing. Inverse Problems, Vol. 31(11): 115002, 2015
• Shuyang Ling and Thomas Strohmer. Blind deconvolution meets blind demixing: algorithms and performance bounds. IEEE Transactions on Information Theory, Vol.63, No.7, pp.4497 - 4520, Jul 2017
• Xiaodong Li, Shuyang Ling, Thomas Strohmer, and Ke Wei. Rapid, robust, and reliable blind deconvolution  via nonconvex optimization. Applied and Computational Harmonic Analysis, 2018
• Shuyang Ling, Ruitu Xu, Afonso S. Bandeira. On the landscape of synchronization networks: a perspective from nonconvex optimization, SIAM Journal on Optimization, Vol.29, No.3, pp.1879-1907, 2019
• Shuyang Ling and Thomas Strohmer. Certifying global optimality of graph cuts via semidefinite relaxation: A performance guarantee for spectral clustering, Foundations of Computational Mathematics, 2019

Education

• PhD, Applied Mathematics
  University of California, Davis
• MS, Statistics
  University of California, Davis