Portrait of Qingyong Zhu

Qingyong Zhu

Assistant Professor
Research Center for Medical AI
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
Shenzhen, China

[Google Scholar] [GitHub] [LinkedIn]

Email: qy.zhu@siat.ac.cn
Gmail: qngyng.zhu@gmail.com
Address: No. 1068 Xueyuan Avenue, Shenzhen University Town, Nanshan District, Shenzhen

AI should be human-centered by design, human-serving by purpose, and guided by the principle
that technological progress ultimately finds its meaning in advancing human well-being.

I am always open to collaborations with students and established researchers alike on optimization and learnable architectures for computational imaging and inverse problems, including geometric deep learning and generative artificial intelligence. Please feel free to contact me by email.

About Me

I earned my master’s degree in Theory and New Technology of Electrical Engineering from Chongqing University of Posts and Telecommunications in 2016. I received my Ph.D. degree in Computational Mathematics from Xi’an Jiaotong University in 2020. From 2020 to 2022, I was a postdoctoral researcher with the Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, where I now serve as an Assistant Professor. My research interests include computational imaging, inverse problems, geometric deep learning, and generative artificial intelligence. I have led or participated in multiple research projects supported by national, provincial, and municipal natural science foundations.

Recent News

Selected Publications

The selected works below list me as first, co-first, or co-corresponding author. * Equal contribution; # Co-corresponding author. For a complete list, please visit my Google Scholar profile.

  1. Q. Zhu, Y. Tan, X. Gu, and D. Liang.
    CogGen: Cognitive-Load-Inspired Fully Unsupervised Deep Generative Modeling for Compressively Sampled MRI Reconstruction.
    arXiv preprint arXiv:2603.04438, 2026.
    [Paper]
  2. A. Cui*, Q. Zhu*, L. Zhang, and S. Xue.
    Logarithmic Function Minimization to Compressed Sensing With Application to Magnetic Resonance Imaging.
    Numerical Algorithms, vol. 101, no. 3, pp. 2043–2067, 2026.
    [Paper]
  3. Q. Zhu, M. Shi, Z.-X. Cui, H. Zeng, and D. Liang.
    Boosting of Mutual-Structure Denoising: A Plug-and-Play Solution for Compressive Sampling MRI Reconstruction With Theoretical Guarantees.
    IEEE Signal Processing Letters, vol. 32, pp. 1880–1884, 2025.
    [Paper]
  4. Q. Zhu, B. Liu, Z.-X. Cui, C. Cao, X. Yan, Y. Liu, J. Cheng, Y. Zhou, Y. Zhu, H. Wang, H. Zeng, and D. Liang.
    PEARL: Cascaded Self-Supervised Cross-Fusion Learning for Parallel MRI Acceleration.
    IEEE Journal of Biomedical and Health Informatics, vol. 29, no. 5, pp. 3086–3097, 2025.
    [Paper]
  5. M. Shi*, Q. Zhu*, B. Liu, and Y. Li.
    Weak Submodularity Implies Localizability: Local Search for Constrained Non-Submodular Function Maximization.
    Discrete Mathematics, vol. 348, no. 2, article 114287, 2025.
    [Paper]
  1. X. Li, Y. Yang#, Q. Zhu#, J. Ma, H. Zheng, and Z. Xu.
    Noise-Generating Mechanism-Driven Implicit Diffusion Prior for Low-Dose CT Sinogram Recovery.
    IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 9, no. 5, pp. 586–597, 2025.
    [Paper]
  2. Z.-X. Cui*, Q. Zhu*, J. Cheng, B. Zhang, and D. Liang.
    Deep Unfolding as Iterative Regularization for Imaging Inverse Problems.
    Inverse Problems, vol. 40, no. 2, article 025011, 2024.
    [Paper]
  3. Q. Zhu, Z.-X. Cui, Y. Liu, J. Cheng, K. Zhao, H. Wang, Y. Zhu, and D. Liang.
    Characteristic-Constrained Accelerating MR T1ρ Mapping With Blockwise Infimal Convolution of Matrix Elastic-Net Regularization.
    Medical Physics, vol. 50, no. 4, pp. 2224–2238, 2023.
    [Paper]
  4. Q. Zhu, B. Liu, Z.-X. Cui, J. Cheng, C. Cao, Y. Liu, D. Liang, and Y. Zhu.
    Accelerated Cardiac Cine MRI Using Spatiotemporal Correlation-Based Hybrid Plug-and-Play Priors (SEABUS).
    Physics in Medicine & Biology, vol. 67, no. 21, article 215008, 2022.
    [Paper]
  5. Q. Zhu, W. Wang, J. Cheng, and X. Peng.
    Incorporating Reference Guided Priors Into Calibrationless Parallel Imaging Reconstruction.
    Magnetic Resonance Imaging, vol. 57, pp. 347–358, 2019.
    [Paper]
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