Qing Qu
Assistant Professor, EECS – Electrical and Computer Engineering
Studies high-dimensional data analysis and nonconvex optimization for machine learning, including the theory of deep and diffusion models and computational imaging.
Qing Qu's lab at Michigan works on learning theory & optimization, generative & foundation models and computational imaging & photography. 41 papers since 2023, 31 at top venues, mostly NeurIPS and ICML. Applications close .
At a glance
- papers, 2023–2026
- 11/ year
- at T1 venues
- 76%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 6 papers2026 · T1 4 · collecting
- 16 papers2025 · T1 11
- 17 papers2024 · T1 15
- 2 papers2023 · T1 1
T1 venuesOtherVenues: NeurIPS, ICML, ICLR, ICASSP, CVPR
Research focuspaper titles that use each topic's words
- Generative & foundation models13 papers
- Learning theory & optimization8 papers
- Computational imaging & photography0 papers
- nonconvex optimization0 papers
- nonconvex0 papers
Paper list
From DBLP.
20266 papers
- ICLR (T1)AlphaFlow: Understanding and Improving MeanFlow Models (opens in a new tab)
- IEEE Signal Process. Mag.An Overview of Low-Rank Structures in the Training and Adaptation of Large Models: From Implicit Low-Dimensionality to Efficient Training and Fine-Tuning [Special Issue on the Mathematics of Deep Learning] (opens in a new tab)
- ICLR (T1)Generalization of Diffusion Models Arises with a Balanced Representation Space (opens in a new tab)
- IEEE Trans. Neural Networks Learn. Syst.Robust Physics-Based Deep MRI Reconstruction via Diffusion Purification (opens in a new tab)
- CVPR (T1)SpaceTools: Tool-Augmented Spatial Reasoning via Double Interactive RL (opens in a new tab)
- ICLR (T1)Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs (opens in a new tab)
202516 papers
- NeurIPS (T1)A Closer Look at Model Collapse: From a Generalization-to-Memorization Perspective (opens in a new tab)
- IEEE Trans. Computational ImagingAnalysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction (opens in a new tab)
- ICML (T1)Attention-Only Transformers via Unrolled Subspace Denoising (opens in a new tab)
- CAMSAPDiffusion Models Learn Low-Dimensional Distributions VIA Subspace Clustering (opens in a new tab)
- CPALEnhancing Video Representation Learning with Temporal Differentiation (opens in a new tab)
- CPALExplaining and Mitigating the Modality Gap in Contrastive Multimodal Learning (opens in a new tab)
- NeurIPS (T1)FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation (opens in a new tab)
- ICLR (T1)Learning Dynamics of Deep Matrix Factorization Beyond the Edge of Stability (opens in a new tab)
- CAMSAPNetwork-Regularized Diffusion Sampling For 3D Computed Tomography (opens in a new tab)
- ICASSP (T1)Sequential Diffusion-Guided Deep Image Prior for Medical Image Reconstruction (opens in a new tab)
- NeurIPS (T1)Shallow Diffuse: Robust and Invisible Watermarking through Low-Dim Subspaces in Diffusion Models (opens in a new tab)
- ICML (T1)SITCOM: Step-wise Triple-Consistent Diffusion Sampling For Inverse Problems (opens in a new tab)
202417 papers
- ICML (T1)A Global Geometric Analysis of Maximal Coding Rate Reduction (opens in a new tab)
- NeurIPS (T1)BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference (opens in a new tab)
- ICML (T1)Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation (opens in a new tab)
- ICASSP (T1)Diffusion-Based Adversarial Purification for Robust Deep Mri Reconstruction (opens in a new tab)
- AISTATS (T1)Efficient Low-Dimensional Compression of Overparameterized Models (opens in a new tab)
- NeurIPS (T1)Exploring Low-Dimensional Subspace in Diffusion Models for Controllable Image Editing (opens in a new tab)
- ICML (T1)Generalized Neural Collapse for a Large Number of Classes (opens in a new tab)
- NeurIPS (T1)Image Reconstruction Via Autoencoding Sequential Deep Image Prior (opens in a new tab)
- CVPR (T1)Improving Training Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architecture (opens in a new tab)
- CPALInvestigating the Catastrophic Forgetting in Multimodal Large Language Model Fine-Tuning (opens in a new tab)
- ICML (T1)Neural Collapse in Multi-label Learning with Pick-all-label Loss (opens in a new tab)
- ICML (T1)Optimal Eye Surgeon: Finding image priors through sparse generators at initialization (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to MichiganFall 2027
Michigan CSE Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
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- Fee
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Not yet confirmed for this intake.
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