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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

Dr Matthias Treder

Dr Matthias Treder

Lecturer

Email
trederm@cardiff.ac.uk
Telephone
+44 (0)29 2251 0960

Academic staff

Dr Catherine Teehan

Dr Catherine Teehan

Placements and employability lecturer

Email
teehanc@cardiff.ac.uk
Telephone
+44 (0)29 2087 9091
Dr Yuhua Li

Dr Yuhua Li

Senior Lecturer

Email
liy180@cardiff.ac.uk
Telephone
+44 (0)29 2087 5317
Dr Alia Abdelmoty

Dr Alia Abdelmoty

Senior Lecturer

Email
abdelmotyai@cardiff.ac.uk
Telephone
+44 (0)29 2087 4751
Dr Jose Camacho Collados

Dr Jose Camacho Collados

Lecturer

Email
camachocolladosj@cardiff.ac.uk
Telephone
+44 (0)29 2087 9108
Dr Alexia Zoumpoulaki

Dr Alexia Zoumpoulaki

Lecturer

Email
zoumpoulakia@cardiff.ac.uk
Telephone
+44 (0)29 2251 0052
Daniela Tsaneva

Daniela Tsaneva

Teaching Associate

Dr Padraig Corcoran

Dr Padraig Corcoran

Director, Data Science Academy
Senior Lecturer

Email
corcoranp@cardiff.ac.uk
Telephone
+44 (0)29 2087 6996

Postgraduate students

No profile image

Steven Arthur

Research student

Email
arthurs@cardiff.ac.uk
No profile image

Liam Hiley

Research student

Email
hileyl@cardiff.ac.uk
No profile image

Harrison Taylor

Research student

Email
taylorh23@cardiff.ac.uk

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.