Qing Qu | HOME

Qing Qu is the PI of DeepThink Lab and 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).

Short Bio: Dr. Qu received my 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. His work has been recognized by a couple of awards, including a Microsoft Ph.D. Fellowship in machine learning in 2016, an NSF Career Award in 2022, an Amazon AWS AI Award in 2023, a UM CHS Junior Faculty Award in 2025, a Google Research Scholar Award in 2025, and 1938E Award in 2026. He was one of the founding organizers of the Conference on Parsimony and Learning (CPAL), area chairs of ICML, NeurIPS, and ICLR, and action editor of TMLR.

Research Interest: Broadly speaking, our research interest lies in the intersection of signal processing, data science, machine learning, and numerical optimization. In particular, I am interested in computational methods for learning low-complexity models from high-dimensional data. The current research of our group focuses on (i) the foundations of generative AI (Slides), (ii) deep representation learning (Slides-1, Slides-2), and (iii) machine learning for scientific applications.

Recruiting: Our group is always looking for self-motivated and talented individuals, please take a look at our Lab’s Join Us for more details.

CV / Google Scholar Profile / DeepThink Lab

Recent Highlights:

Student Award: Muhammad Ashiq received the 2026 NSF Graduate Fellowship (GRFP)!
Grant Approval: Received Google TPU Research Awards. Thanks Google Research for TPU computing support!
Workshop: Organizing a workshop on “Foundations of Deep Generative Models: Understanding Memorization, Generalization, and Reasoning” at ICML’26!
Student Award: Huijie Zhang received 2026-2027 Rackham Predoctoral Fellowship
PI Award: PI Qu received the 1938E Award in UM College of Engineering (news, a prestigous CoE award for an assistant professor; awarded annually to one faculty member for excellence in teaching, mentoring, and service)
Student Award: Can Yaras received the 2026 CPAL Rising Star Award
Upcoming Tutorial:
- ESSAI 2026 (with Sam Buchanan. Yi Ma, Zhihui Zhu): Learning Deep Low-Dimensional Models from High-Dimensional Data: From Theory to Practice
- ICASSP 2026 and ISIT 2026 (with Yuxin Chen, Yuting Wei, Liyue Shen): Harnessing Low Dimensionality in Diffusion Models: From Theory to Practice
Paper Acceptance: 3 papers at ICLR’26, 2 papers at AISTATS’26, 1 paper at CPAL’26
- Generalization of Diffusion Models Arises with a Balanced Representation Space (ICLR’26)
- AlphaFlow: Understanding and Improving MeanFlow Models (ICLR’26)
- Unlearning Isn’t Invisible: Detecting Unlearning Traces in LLMs from Model Outputs (ICLR’26)
- Understanding How Nonlinear Layers Create Linearly Separable Features for Low-Dimensional Data (AISTATS’26)
- Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective (AISTATS’26)
- Analyzing and Improving Model Collapse in Rectified Flow Models (CPAL’26, oral, top 7.5%)
Workshop: Co-organizing 2nd DeLTa Workshop (Deep Generative Model in Machine Learning: Theory, Principle and Efficacy) at ICLR’26.
Lectures: Gave 3 Lectures at IAISS’25: (Lecture I, Lecture II, Lecture III)
Paper Acceptance: 6 papers have been accepted to NeurIPS’25 (3 spotlight + 3 posters)
- Towards Understanding the Mechanisms of Classifier-Free Guidance (spotlight, top 3.2%)
- Shallow Diffuse: Robust and Invisible Watermarking through Low-Dimensional Subspaces in Diffusion Models (spotlight, top 3.2%, website)
- A Closer Look at Model Collapse: From a Generalization-to-Memorization Perspective (spotlight, top 3.2%, website)
- Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modeling
- FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation
- UGoDIT: Unsupervised Group Deep Image Prior Via Transferable Weights
Tutorial (Website): Presenting a full-day tutorial at ICCV’25 on “Learning Deep Low-Dimensional Models from High-Dimensional Data: From Theory to Practice“, together with Profs. Yi Ma, Liyue Shen, Atlas Wang, Zhihui Zhu , and Dr. Sam Buchanan (Oct. 2025)
Tutorial (Website): Presenting a 2.5-hour tutorial at ICML’25 on “Harnessing Low Dimensionality in Diffusion Models: From Theory to Practice” with Prof. Liyue Shen and Prof. Yuxin Chen, (Jul. 2025)

Recent Selected Publications:

Can Yaras, Peng Wang, Laura Balzano, Qing Qu (2024). Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation. International Conference on Machine Learning (ICML’24), 2024. (Oral, top 1.5%, best poster award at MMLS’24)
Preprint – PDF – BibTex – Code
Huijie Zhang*, Jinfan Zhou*, Yifu Lu, Minzhe Guo, Liyue Shen, Qing Qu (2023). The Emergence of Reproducibility and Consistency in Diffusion Models. International Conference on Machine Learning (ICML’24), 2024. (Best Paper Award at NeurIPS’23 Workshop on Diffusion Models, news)
Preprint – PDF – BibTex – Slides – Website
Zhihui Zhu*, Tianyu Ding*, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu (2021). A Geometric Analysis of Neural Collapse with Unconstrained Features. Neural Information Processing Systems (NeurIPS’21), 2021. (spotlight, top 3%)
Preprint – PDF – Slides – BibTex – Code – Video
Qing Qu, Yuexiang Zhai, Xiao Li, Yuqian Zhang, Zhihui Zhu (2020). Analysis of the Optimization Landscapes for Overcomplete Representation Learning. International Conference on Learning Representations (ICLR’20), 2020. (Oral, top 1.9%)
Preprint – PDF – Slides – BibTex
Qing Qu, Xiao Li, Zhihui Zhu (2019). Exact Recovery of Multichannel Sparse Blind Deconvolution via Gradient Descent. SIAM Journal on Imaging Science, 13(3): 1630–1652, 2020. (NeurIPS’19, spotlight, top 3%).
Preprint – PDF – Code – Poster – Slides – BibTex
Ju Sun, Qing Qu, John Wright (2018). A Geometric Analysis of Phase Retrieval. Foundations of Computational Mathematics, 18(5):1131–1198, 2018. (ISIT’16)
Preprint – PDF – Code – Slides – BibTex