Michael Davies

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Michael Davies

PROF

 https://orcid.org/0000-0003-2327-236X

Accepting PhD Students

PhD projects

I am currently looking for PhD students under the following themes listed below. There is the opportunity to get university funding. As these are highly competitive, you are recommended to complete a formal application as soon as possible in order to be considered for any university funding opportunities.

Current Research Themes

Machine Imaging
Computational imaging relies on the acquisition of sensor measurements that indirectly inform about the imaged object and has a broad range of applications, from computational microscopy, medical imaging (CT, MRI, ultrasound), to sonar, radar, and seismic imaging. Current state-of-the-art methods are leveraging sophisticated machine learning (ML) solutions based on deep neural networks. However, supervised ML solutions necessitate unrealistic access to a large quantity of ground truth images.

One of the aims of this theme is to develop a foundational theoretical framework and algorithmic toolbox for learning to image with limited or no ground truth data. It will lay the foundations for a new wave of unsupervised ML-based computational imaging, with potential applications across a range of settings and imaging modalities from advanced medical imaging to robotics and autonomous systems. Unleashing the ML from ground truth data will enable the algorithms to exploit the larger quantities of unsupervised measurement data available to learn more complex and effective models leading to practical benefits of accelerated acquisitions and reduced imaging artifacts, as well as totally new imaging opportunities.

Data-Driven Computational Sensing and Imaging
Today's state-of-the-art imaging and sensing rely as much on computation as they do on sensor hardware. Furthermore, computational sensing and imaging is increasingly exploiting data-driven and machine learning solutions to enhance performance and develop novel hardware/software co-designed sensing systems. However, in critical scenarios such as medicine or defence and security it is vital that verifiable algorithmic solutions are used, which places restrictions on which machine learning approaches are admissible. Importantly, fully black box machine learning solutions should be avoided. This theme will therefore focus on the development of novel algorithmic and mathematical frameworks to exploit data and machine learning for imaging and sensing within a controlled explainable and verifiable manner. There will be a specific focus on RF and electro-optic/IR sensor modalities.

Sensor and Information Fusion
Sensor networks, sensor fusion and management techniques address key challenges in intelligence, surveillance, target acquisition, and reconnaissance (ISTAR). Opportunities in adaptive data-driven sensor tasking and resource management include adaptive sensor placement, adaptive waveform design to reflect the target reflection characteristics and channel environments, and adaptive sensor selection. Although these problems have solutions in specific use cases, this theme will consider scenarios with broader applications involving multiple heterogeneous sensors on single or multiple cooperative autonomous airborne platforms.

The solutions developed in this should be robust to dynamic and congested environments, adverse weather conditions, and mutual sensor interference. A range of algorithmic and signal processing or machine learning technologies will be considered, as well as specific technical challenges. For example, projects in this theme will consider aspects related to wide area motion imaging (WAMI), position, navigation, and timing issues (PNT); robustness to adversarial attack; sensor fusion and tracking applications; use of kernel and Monte Carlo methods; outlier-robust (and other metrics) messages in belief propagation algorithms; and scheduling in large dynamic networks. Probabilistic and Bayesian frameworks will be preferred to enable uncertainty quantification and management.

Personal profile

Biography

Career History

Mike Davies holds the Jeffrey Collins Chair in Signal and Image Processing in the School of Engineering at the University of Edinburgh where he is currently Director of Research. He is a leading expert in the mathematical theory and algorithm design for computational imaging, compressed sensing, and machine learning systems. From 2013-24 he led the University Defence Research Collaboration (UDRC) a major UK research programme for signal processing in defence in collaboration with Dstl. He has served on various professional committees and consulted for both UK government and industry on signal processing and AI. His personal research has been funded by over 30 competitively awarded research contracts from UK Research Councils, the Royal Society, UK Ministry of Defence (MOD), industry and EU, totalling over £30M.

Awards and Fellowships

European Association for Signal Processing (EURASIP) Group Technical Award (2025)

IET Achievement Medal in Signal Processing (2024)

Visiting Professor ENS Lyon, France (2023-24)

Fellow of the Royal Society of Edinburgh (2018)

Fellow of the Royal Academy of Engineering (2017)

Royal Society Wolfson Research Merit Award (2016)

Fellow of the European Association of Signal Processing (EURASIP) (2016)

Fellow of the IEEE (2015)

Texas Instruments Distinguished Visiting Professor, Rice University (2012)

 

Research Interests

His work focusses on the sparse representations, low dimensional models and compressed sensing (CS), and their application to various signal processing, imaging and machine learning challenges. During the past decade he has made significant contributions to the key areas of fundamental CS theory, low dimensional signal models, the development and analysis of new reconstruction algorithms, and a new theoretically grounded unsupervised learning framework for machine imaging (equivariant imaging).

He has applied these ideas to a number of applications including: chemical identification in Raman spectroscopy, dynamic MRI and 3D brain imaging, a new compressed sensing framework for quantitative MRI, electronic surveillance and Synthetic Aperture Radar.

Current Projects

Centre for Doctoral Training in Sensing, Processing, and AI for Defence and Security (SPADS)

The SPADS doctoral programme will train the next generation of highly professional defence scientists, including engineers, computer scientists, and mathematicians, capable of leading developments in cutting-edge and generation-after-next technologies in information and communication technology that are poised to transform not only the world of defence & security, but also the broader civilian society. 

Compact, high-resolution 3D vision - sketched LIDAR demonstrator

An EPSRC Impact Accelerator Award to build a hardware demonstrator for the sketched LIDAR data compression technology that was developed in my ERC advanced grant C-SENSE.

Leonardo Prosperity Partnership: Smart Products made Smarter

The low volume, highly complex sensor systems produced by Leonardo present complex engineering challenges for design and production. Advances in machine learning, cobotics, novel materials, additive manufacturing, digital twinning and signal & image processing provide new paradigms for the end-to-end design and production processes and requires the development of a fully integrated digital design, assembly and manufacturing capability.

 

Past Projects

UDRC: University Defence Research Collaboration in Signal Processing

An academia led partnership between the defence industry, academia and the government sector. The UDRC develops research in signal processing with application to the defence industry.

ERC Advanced Grant: C-SENSE, "Exploiting Low Dimensional Models in Sensing, Computation and Processing"

The aim of this project is to develop the next generation of compressive and computational sensing and processing techniques.

CQ-MRI: EPSRC funded award in Compressed Quantitative MRI

The proposed research will provide the first proof-of-principle for a new family of Compressed Quantitative Magnetic Resonance Imaging (CQ-MRI), able to rapidly acquire a multitude of physical parameter maps for the imaged tissue from a single scan.

CIRI: EPSRC funded award in Compressed Imaging in Radio Interferometry

The CIRI project aims to bring new advances for interferometric imaging for next-generation radio telescopes, together with theoretical and algorithmic evolutions in generic compressive imaging.

EU Innovative Training Networks:

SpaRTaN: Sparse Representations and Compressed Sensing Training Network

MacSeNet: Machine Sensing Training Network

Professional Activities

2014 – date         Chair of Sensor Signal Processing for Defence Conference

2013 – 2020         Member of IEEE Sig. Proc. Theory and Methods technical committee

 

Education/Academic qualification

Doctor of Philosophy (PhD), Noise Reduction in Nonlinear Time Series Analysis, University College London

Award Date: 1 Dec 1993

Bachelor of Arts, University of Cambridge

Award Date: 1 Jan 1989

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