Biography

Micheal Davies is a Professor of Signal and Image Processing at the Institute for Digital Communications, University of Edinburgh, a position he has held since 2006. He is a leading expert in digital signal and image processing, with a career spanning academia and industry over three decades.
He graduated with a First-Class Honours degree in Engineering from the University of Cambridge in 1989, followed by a PhD in Nonlinear Dynamics from University College London in 1993. Before joining Edinburgh, he held academic positions at Queen Mary University of London and King’s College London, and also worked in industry as a Senior Principal Engineer at Central Research Laboratories Ltd in Hayes.
His work has been recognised with several prestigious honours. He was elected Fellow of the Royal Academy of Engineering in 2017, and Fellow of the Royal Society of Edinburgh in 2018.

Collaboration with LPENSL

The visit of Professor Michael E. Davies, a world-leading expert in signal and image processing, will be a major asset to the SiSyPh team at ENS de Lyon. His pioneering work in sparse representations, compressed sensing, and machine learning aligns closely with the team’s ongoing research in imaging inverse problems and self-supervised learning.
The visit supports an active collaboration with J. Tachella focused on developing new self-supervised deep learning methods that eliminate the need for ground-truth data—an essential breakthrough for scientific imaging. Together, they aim to establish theoretical foundations and explore new algorithmic strategies.
Beyond the core collaboration, Prof. Davies' stay will benefit the wider SiSyPh team, particularly researchers working on inverse problems and statistical signal processing, such as N. Pustelnik. His strong background in AI and deep learning complements the team’s strategic direction and will enrich its interdisciplinary research environment.
Prof. Davies will also contribute to the scientific life of ENS de Lyon through a talk at the Machine Learning & Signal Processing (MLSP) seminar, a public colloquium on self-supervised learning for scientific imaging, and a series of lectures for master's students in physics and computer science.
His visit is expected to stimulate collaborations, deepen theoretical understanding, and advance innovative research at the interface of physics, signal processing, and AI.

Major publications

• M. E. Davies, G Puy, P Vandergheynst, Y Wiaux, 2014, A compressed sensing framework for magnetic resonance fingerprinting. SIAM J. Imaging Sci., 7(4), 2623–2656.
• G. Puy, M. E. Davies, R. Gribonval, 2017, Recipes for stable linear embeddings from Hilbert spaces
  to Rm. IEEE Transactions on Information Theory, vol 63(4), pp 2171-2187.
• M. P. Sheehan, J. Tachella, M. E. Davies, A Sketching Framework for Reduced Data Transfer in
  Photon Counting Lidar. IEEE Trans. Computational Imaging, vol. 7, pp. 989-1004, 2021, doi: 10.1109/
  TCI.2021.3113495.
• D Chen, J. Tachella, M. E. Davies, 2021, Equivariant Imaging: learning beyond the range space.
  IEEE International Conference on Computer Vision (ICCV) 2021.
• J. Tachella, D Chen, M. E. Davies, Sensing Theorems for Unsupervised Learning in Linear Inverse Problems. Journal of Machine Learning Research, 24, pp 1-45, 2023.