Shuyang Ling is an Assistant Professor of Data Science at NYU Shanghai and a Global Network Assistant Professor at the Tandon School of Engineering 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
Education
- PhD, Applied Mathematics
University of California, Davis - MS, Statistics
University of California, Davis
- Mathematics of Signal Processing
- Machine Learning
- Optimization
- Compressive Sensing
- Computational Harmonic Analysis
- The Mathematics of Statistics and Data Science
- Independent Study: Mathematics