Shuyang Ling

Shuyang Ling
Assistant Professor of Data Science, NYU Shanghai; Global Network Assistant Professor, NYU

Shuyang Ling is an Assistant Professor of Data Science at NYU Shanghai and a Global Network Assistant Professor at NYU. 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. 

Read more in Faculty Spotlight: Ling Shuyang.


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

Research Interests

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

Courses Taught

  • The Mathematics of Statistics and Data Science
  • Independent Study: Mathematics