Billy Junqi Tang (唐俊祺)


Hi! I am a Research Associate with Department of Applied Mathematics and Theoretical Physics, University of Cambridge. My research interests include large-scale optimization, statistical learning theory, with applications in computer vision and medical imaging. Most recently, my research has been focusing on the theoretical foundations of non-convex optimization in computational imaging with deep-learning-based image priors, and efficient deep unrolling networks for medical imaging.

In 2019, I completed my Ph.D in the University of Edinburgh under the supervision of Prof. Mike Davies and the project was fully funded by EU H2020 project MacSeNet Innovative Training Network. Prior to that, I had 4 wonderful years in Sichuan University, China as a undergrad majoring in Communication Engineering from 2010 to 2014.

Recent highlights/Preprints:

  1. Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb.  Stochastic Primal-Dual Deep Unrolling Networks for Imaging Inverse Problems. 2021 [Preprint]
  2. Junqi Tang, Mike Davies.  A Fast Stochastic Plug-and-Play ADMM for Imaging Inverse Problems. 2020 [Preprint]


  1. Derek Driggs*, Junqi Tang*, Jingwei Liang, Mike Davies, Carola-Bibiane Schönlieb. SPRING: A Fast Stochastic Proximal Alternating Method for Non-smooth Non-convex Optimization.  SIAM Journal on Imaging Sciences (to appear), 2021 [PDF][Code]
  2. Julian Tachella, Junqi Tang, Mike Davies. The Neural Tangent Link Between CNN Denoisers and Non-local Filters.  IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR, oral), 2021 [PDF][Code]
  3. Junqi Tang, Karen Egiazarian,  Mohammad Golbabaee, Mike Davies. The Practicality of Stochastic Optimization in Imaging Inverse Problems. IEEE Transactions on Computational Imaging, 2020 [PDF]
  4. Junqi Tang, Karen Egiazarian,  Mike Davies. The Limitation and Practical Acceleration of Stochastic Gradient Algorithms in Inverse Problems. International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 [PDF] [slides]
  5. Junqi Tang, Mohammad Golbabaee, Francis Bach,  Mike Davies. Rest-Katyusha: Exploiting the Solution’s Structure via Scheduled Restart Schemes. Advances in Neural Information Processing Systems (NeurIPS), 2018.[PDF] [Poster]
  6. Junqi Tang, Mohammad Golbabaee, Mike Davies. Exploiting the Structure via Sketched Gradient Algorithms. IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2017.[PDF]
  7. Junqi Tang, Mohammad Golbabaee, Mike Davies. Gradient Projection Iterative Sketch for Large-Scale Constrained Least-Squares. International Conference on Machine Learning (ICML), 2017.[PDF] [slides]


  1. Randomized Structure-Adaptive Optimization. PhD thesis, 2019 [link]
  2. The Non-Uniform Fast Fourier Transform in Computed Tomography. MSc thesis, 2015 [link]


Reviewer of:

NeurIPS 2021, ICML 2021, CVPR 2021, ICLR 2021, NeurIPS 2020, NeurIPS 2019

IMA Journal on Numerical Analysis,

Numerical Algorithms,

Mathematics of Operations Research,

IEEE Transactions on Neural Networks and Learning Systems


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