Qing QuAssistant Professor
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Qing Qu
Qing QuAssistant ProfessorElectrical Engineering and Computer Science
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 Institute for Data Science (MIDAS), the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM), and the Michigan Institute for Computational Discovery and Engineering (MICDE).

I received my B.E. degree from Tsinghua University, Beijing, China, in 2011, and obtained my Ph.D. degree from Columbia University with Prof. John Wright in 2018. I was a Moore-Sloan fellow at NYU Center for Data Science from 2018 to 2020. My work has been recognized by a couple of awards, including a Microsoft Ph.D. Fellowship in machine learning, a NSF Career Award in 2022, and an Amazon AWS AI Award in 2023.

My research interest lies in the intersection of signal processing, data science, machine learning, and numerical optimization. Broadly speaking, I am 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 and scientific discovery. Recently, my major interest has been in understanding deep representation learning and diffusion models, through the lens of low-dimensional modeling.

Here is my Google Scholar Profile. Find me on LinkedIn and Twitter.

For prospective students: My group recruits 1-2 PhD students per year, and I am looking for self-motivated students with aligned research interests to work with. To help identify mutual interests, please fill out this form and send me a short email note with your Resume.

News

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Recent Talks:

SLowDNN talk: Understanding Deep Neural Networks via Neural Collapse

CSP Seminar Talk: Invariant Low-Dimensional Subspaces in Gradient Descent for Learning Deep Networks

Funding Acknowledgement

Our group acknowledge generous support from the National Science Foundation (NSF), Office of Naval Research (ONR), Amazon Research, KLA Corporation, MICDE, and MIDAS