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Qing Qu
1301 Beal AvenueAnn Arbor, MI 48109-2122

Qing Qu is an assistant professor in the ECE Division of the Electrical Engineering and Computer Science department of the College of Engineering, University of Michigan – Ann Arbor. He is also affiliated with the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM), and the Michigan Institute for Data Science (MIDAS).

He received his B.E. degree from Tsinghua University, Beijing, China, in 2011, and obtained his Ph.D. degree from Columbia University with Prof. John Wright in 2018. He was a Moore-Sloan fellow at NYU Center for Data Science from 2018 to 2020. He is the recipient of Best Student Paper Award at SPARS’15 (with Ju Sun and John Wright), and the recipient of the 2016 Microsoft Ph.D. Fellowship in machine learning. He received the NSF Career Award in 2022.

His research interest lies in the intersection of signal processing, data science, machine learning, and numerical optimization. He is particularly interested in computational methods for learning low-complexity models from high-dimensional data, leveraging tools from machine learning, numerical optimization, and high dimensional geometry, with applications in imaging sciences, scientific discovery, and healthcare. Recently, he is also interested in understanding deep networks through the lens of low-dimensional modeling. You can learn more about his research here.

Here is his Google Scholar Profile. Find him on LinkedIn and follow him on Twitter.

Selected Publications

Zhihui Zhu (equal), Tianyu Ding (equal), Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu (2021). A Geometric Analysis of Neural Collapse with Unconstrained Features. (NeurIPS’21, spotlight, top 3%)
Preprint – PDFSlides – BibTex

Qing Qu, Yuexiang Zhai, Xiao Li, Yuqian Zhang, Zhihui Zhu (2020). Analysis of the Optimization Landscapes for Overcomplete Representation LearningICLR’20oral, top 1.9%.
Preprint – PDF – SlidesBibTex

Qing Qu, Xiao Li, Zhihui Zhu (2019). Exact Recovery of Multichannel Sparse Blind Deconvolution via Gradient DescentSIAM Journal on Imaging Science, 13(3): 1630–1652, 2020. (NeurIPS’19spotlight, top 3%).
Preprint – PDF – Code – Poster – SlidesBibTex

Yuqian Zhang, Qing Qu, John Wright (2020). From Symmetry to Geometry: Tractable Nonconvex ProblemsIn Submission.
Preprint – PDF –  SlidesBibTex

Ju Sun, Qing Qu, John Wright (2018). A Geometric Analysis of Phase RetrievalFoundations of Computational Mathematics, 18(5):1131–1198, 2018. (ISIT’16).
Preprint – PDF – Code – SlidesBibTex