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New York University Shanghai, Office Number: S750
567 West Yangsi Road
Pudong New District Shanghai, 200126
Email: sl3635 at nyu dot edu
Phone: +86(21)20596120.

About me:
I am currently a Tenure-Track Assistant Professor of Data Science at New York University Shanghai and NYU Global Network Assistant Professor.

I was an Assistant Professor/Courant Instructor at Courant Institute of Mathematical Sciences (CIMS) and Center for Data Science from 2017 to 2019. I worked closely with the Math and Data Group. I received Ph.D. degree in 2017 at Department of Mathematics, University of California, Davis, under the supervision of Prof. Thomas Strohmer.

Here is my CV and Google Scholar Page.

Ph.D. students: NYU Ph.D. program Shanghai track in Data Science admission for fall 2024 is now open! The deadline for application is Dec 5, 2023. Please check here and here for details. If you are interested in joining NYU Shanghai and working with me, please indicate your preference in the application system. We welcome all candidates with strong background in mathematics, statistics, computer science, and relevant areas to apply. We will hold an online doctoral admissions information session at 12pm, Oct 31, 2023, Shanghai time. If you are interested, please register your name and contact information via this link.

Post-doctoral Research Fellow Hiring: Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning at NYU Shanghai has several post-doctoral positions available. The positions come with competitive salary and benefits. The post-doctoral research fellows are expected to work on topics related to the mathematical foundations of data science and machine learning. Please apply via this link. You may find more information here. Interested applicants with background in applied mathematics, optimization, probability and statistics, and computer science are also very welcome to send your CV, representative works, and research statement to me.

Seminar and reading groups:

Research Interests:

  • Mathematics of signal processing and machine learning

  • Iterative algorithms, convex and non-convex optimization, optimization landscape

  • Compressive sensing, low-rank matrix recovery, blind deconvolution, group synchronization, spectral methods in data science

  • Inverse problems in image processing and signal processing

  • Computational harmonic analysis, random matrix, spectral graph theory