Machine learning and artificial intelligence

Machine learning describes a collection of statistical and computational techniques for identifying patterns within data. More generally, other artificial intelligence techniques such as evolutionary algorithms, neural networks, and deep learning play an important role in identifying large scale networks or bioinformatics problems with small data.

Our expertise

Advanced machine learning
Attractor reconstruction methods for physiological data combined with machine learning
Bayesian machine learning
Ensemble learning and drop-out learning
Evolutionary multi-objective machine learning

Evolutionary optimisation
Neural networks and deep learning
Secure machine learning
The identification of classifiers/predictors of clinical outcomes.

Meet the team

Members

Professor Philip Aston

Professor of Mathematics

Dr Roman Bauer

Senior Lecturer

Dr Alex Couto Alves

Lecturer in Bioinformatics and Statistical Genomics

Professor Yaochu Jin

Distinguished Chair, Professor in Computational Intelligence

Dr Marika Kaakinen

Lecturer in Statistical Multi-omics

Dr Samaneh Kouchaki

Senior Lecturer in Machine Learning for Healthcare

Professor Paul Krause

Emeritus Professor

Dr Emma Laing

Visiting Professor

Dr Agnieszka Lemanska

Senior Lecturer in Health Data Science

Dr Sotiris Moschoyiannis

Reader in Complex Systems

Dr Joaquin Prada

Senior Lecturer in Epidemiology

Professor Inga Prokopenko

Professor e-One Health and Head of Statistical Multi-Omics

Professor H Lilian Tang

Professor in Artificial Intelligence

Professor Kevin Wells

Professor of AI in Human and Veterinary Healthcare

Associate members

Professor Jo Armes

Professor of Cancer Care and Lead for Digital Health

Dr Matteo Barberis

Faculty Theme Leader for Infection and Immunity, Faculty Coordinator of Centre for Mathematical and Computational Biology (CMCB), and Reader in Systems Biology