Shuyang Ling

Assistant Professor of Data Science at NYU Shanghai


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. 

Research Interests

  • Mathematics of Signal Processing
  • Machine Learning
  • Optimization
  • Compressive Sensing
  • Computational Harmonic Analysis

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


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