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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).
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, and Amazon AWS AI Awards in 2023.
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 and scientific discovery. Recently, his major interest lies in understanding deep networks through the lens of low-dimensional modeling.
Here is his Google Scholar Profile. Find him on LinkedIn and follow him on 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
Recent Highlights:
- Tutorials on nonconvex optimization for deep representation learning (from ACDL’23):
- A new short course on Learning Nonlinear and Deep Low-Dimensional Representations from High-Dimensional Data.
- Program chair of our new Conference on Parsimony and Learning (CPAL).
- I am organizing the ECE Communications and Signal Processing Seminar at UMich every Thursday (Videos). Send me an email if you are interested to give a talk in person.
Upcoming Events:
- Allerton Conference (Sept. 2023)
- Symposium at MIDAS: Generative AI: Diffusion Models for Scientific Machine Learning (Sept. 15th, 2023)
- CAMSAP’23 Special Session: Learning and Optimization for Computational Imaging (Dec. 2023)
- Conference on Parsimony and Learning (CPAL) (Jan. 2024)
Recent News:
- Talk: Presentation at UM CSP Seminar, slides, video (Sept. 2023)
- Public service: Invited to be an Area Chair of ICLR’24 (Aug. 2023)
- Paper release: Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency (Jul. 2023)
- Grant approval: Received a new three-year NSF RI medium grant in collaboration with Prof. Zhihui Zhu (OSU) and Prof. Jeremias Sulam (JHU), for supporting our research on deep representation learning via neural collapse (Jun. 2023).
- Paper release: The Law of Parsimony in Gradient Descent for Learning Deep Linear Networks (Jun. 2023)
- Outreach: Gave four lectures at 6th Advanced Course on Data Science & Machine Learning (ACDL‘23) (Jun. 2023)
- Outreach: Gave an educational course at ICASSP’23: Learning Nonlinear and Deep Low-Dimensional Representations from High-Dimensional Data: From Theory to Practice (Jun. 2023)
- Public service: Invited to be an Area Chair of NeurIPS’23 (Apr. 2023)
- Grant approval: received a gift grant from Amazon Research Awards. (Mar. 2023)
- Public service: Organized and gave a talk at the 3rd SLowDNN Workshop from Jan. 3rd to 6th at MBZUAI, Abu Dhabi, MBZUAI. (Jan. 2023)
Archived:
Funding Acknowledgement
Our group acknowledge generous support from the National Science Foundation (NSF), Office of Naval Research (ONR), Amazon Research, KLA Corporation, and MIDAS
