Data analytics and machine learning
We deal with the analysis and visualisation of complex data, development of machine learning algorithms and optimisation techniques.
The availability of large amounts of data - such as from databases, streams, or internet of things (IoT) devices - requires efficient methods for facilitating its analysis, categorisation, distribution, and visualisation.
The data analytics branch of our group addresses this challenge using ensemble and federated learning, approximation techniques, and optimised allocation of tasks in distributed computing environments.
The machine learning branch develops algorithms for the prediction and interpretation of diverse datasets ranging from medical images to video streams.
Aims
We aim to facilitate cooperation with research groups outside of the School of Computer Science.
There are two branches to our group.
Data analytics
Our work in data analytics largely focuses on efficient large scale implementation of machine learning models across distributed environment, and effective deployment of machine learning systems across various platforms:
- Partitioning machine learning algorithms across distributed environments (such as cloud and IoT) for both real-time (stream processing) and batch learning.
- Ensemble and federated learning across distributed systems platforms.
- Container frameworks (such as Docker, Kubernetes) and in-network capability (such as Network Function Virtualisation and MiddleBox approaches) to support machine learning.
- Approximation techniques to trade off accuracy and execution time (convergence time).
Machine learning
Our expertise in machine learning involves fundamental research on new algorithm development and applied research on solving real data rich problems to benefit economy and society:
- Development of novelty detection / anomaly detection algorithms.
- Development of algorithms for instance selection and datasets shift detection.
- Development of semi-supervised learning algorithms in medical imaging data applications (such as MRI, microscopy).
- Development of a machine learning toolbox.
- Explainable machine learning for interpreting model results.
- Application of machine learning to problems in healthcare, service industries, security, etc.
Lead researcher
Academic staff
Dr Padraig Corcoran
Director of Research
Senior Lecturer
- corcoranp@cardiff.ac.uk
- +44 (0)29 2087 6996
Dr Andreas Artemiou
Deputy Director, Data Science Academy
Reader
- artemioua@cardiff.ac.uk
- +44 (0)29 2087 0616
Professor Scott Orford
Professor in Spatial Analysis and GIS
- orfords@cardiff.ac.uk
- +44 (0)29 2087 5272
Professor Alun Preece
Professor of Intelligent Systems
Co-Director of the Security, Crime and Intelligence Innovation Institute
Deputy Head of School
- preecead@cardiff.ac.uk
- +44 (0)29 2087 4653
Dr Bahman Rostami-Tabar
Reader in Management Science and Business Analytics
- rostami-tabarb@cardiff.ac.uk
- +44 (0)29 2087 0723
Postgraduate students
Associated staff
There are regular talks in the Artificial Intelligence and Data Analytics Seminar.
Joint seminars are held with the Machine Learning group at University of Waikato.