Machine learning

Machine learning is the science of constructing algorithms that learn from data and are therefore able to adapt to changing data.

The proliferation of data and the availability of high performance computing makes this a fertile and very applicable area of research. It draws on ideas in computer science, statistics and applied mathematics, together with biologically inspired paradigms such as neural computation.

Machine learning research at Exeter spans the range of data, applications and methodologies: from kernel methods to deep neural architectures and reinforcement learning applied to both continuous and discrete, graph-based data.

The group collaborates with industrial partners and with the Impact Lab, and contributes strongly to the University’s membership of the Alan Turing Institute

Group members

Current research projects

Computational fluid dynamics (CFD) is fundamental to modern engineering design, from aircraft and cars to household appliances. It allows the behaviour of fluids to be computationally simulated and new designs to be evaluated. However, each CFD evaluation can take a long time, possibly hours or days.

This 3-year EPSRC-funded project aims to accelerate the optimisation process by substituting computationally simpler, dynamically generated 'surrogate' models in place of full CFD evaluation - learning appropriate surrogates from a relatively few well-chosen full evaluations, with multiple competing quality criteria for the final designs.

Our diverse range of industrial collaborations will ensure research is driven by realistic industrial problems and builds on existing industrial experience.

Project team

Professor Richard Everson (project lead)

Professor Jonathan Fieldsend

Professor Gavin Tabor (Engineering)

Funded by Innovate UK, this project is learning the best parameters to enable digital twins to faithfully represent real-world systems.

Contact: Professor Richard Everson

Machine learning and machine vision are being used to automatically detect and classify defects in waste-water pipes from CCTV inspections in this collaboration with Innovate UK and South West Water.

Contact: Professor Richard Everson

Developing novel methods for modelling and reconstructing the 3D geometry and dynamics of the human face from image data, with unprecedented quality. Read more on the Computer vision page

Contact: Dr Anastasios (Tassos) Roussos

This project is using machine learning methods to model the sea’s surface from maritime radar observations, and then using the model to make predictions of that surface for up to two minutes into the future.

Contact: Dr Jacqueline Christmas

We are using our knowledge of the constraints on the shapes of sea waves and their spectra, to learn the scale and shapes of waves from video recorded by a single, monoscopic camera installed on a ship.

Contact: Dr Jacqueline Christmas

This project involves developing image processing techniques, based on inexpensive, off-the-shelf digital photographic equipment, to model the shape of a surface and hence read its inscriptions.

Contact: Dr Jacqueline Christmas

We are working with the Royal Society to develop collaborative strategies to enable multiple drones to efficiently detect marine oil spills.

Contact: Dr Chunbo Luo

Using deep learning to enhance infrared images via super resolution and detect/recognise low-pixel targets. This project is a collaboration with CENSIS, Thales and the Scottish Funding Council (SFC).

Dr Chunbo Luo

This interdisciplinary ESRC-funded project uses machine learning, text analysis and network science to understand how people are exposed to political news online. Find out more on the ExpoNet webpage

Contact: Dr Hywel Williams

Wind storms can cause great damage to property and infrastructure. The windstorm footprint is an important summary of the hazard, of great relevance to the insurance industry and to infrastructure providers.

Windstorm footprints are conventionally estimated from meteorological data and numerical weather model analyses. However there are several interesting, less structured data sources (video feeds, social media data, amateur meteorological observations) that could contribute to their estimation, and more importantly will raise the spatial resolution of estimates.

These will be investigated as part of this 4-year NERC-funded project which will develop and compare statistical process modelling and machine learning approaches to the problem.

Project team

Professor Peter Challenor (Mathematics, project lead)

Professor Richard Everson

Dr Theo Economou (Mathematics)

Professor Jonathan Fieldsend

Dr Chunbo Luo

Professor David Stephenson (Mathematics)

Dr Hywel Williams

Funded by the ESRC, this project considers social media as a data source to monitor pollen and air pollution alongside their health impacts such as hayfever and asthma. Visit the social sensing Exeter website.

Contact: Dr Hywel Williams

This project is developing our understanding the behaviour of recurrent neural networks with dynamical systems theory.

Contact: Dr Lorenzo Livi 

We are designing a statistical-geometrical framework to perform change detection on sequences of attributed graphs of varying size. This project is in collaboration with the Swiss National Science Foundation (SNF).

Contact: Dr Lorenzo Livi

Using machine learning to predict and control epileptic seizures in drug-resistant patients.

Contact: Dr Lorenzo Livi