Qing Qu
Assistant Professor · College of Engineering · University of Michigan-Ann Arbor
Quick answer: Qing Qu is Assistant Professor at University of Michigan-Ann Arbor. Qing Qu shows an active PhD hiring signal as of 2026-09-11.
⭐ Our group is always looking for self-motivated and talented individuals, please take a look at our Lab's Join Us for more details.
Research interests
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.
Selected publications (since 2023)
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)
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)
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%)
Qing Qu, Yuexiang Zhai, Xiao Li, Yuqian Zhang,
Frequently asked questions
Is Qing Qu hiring PhD students at University of Michigan-Ann Arbor?
Yes. As of 2026-09-11, Qing Qu's faculty page shows a PhD hiring signal: Our group is always looking for self-motivated and talented individuals, please take a look at our Lab's Join Us for more details..
What does Qing Qu research?
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
Data last updated: 2026-09-11 · Source: phd-match.com faculty database.
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