Professor Ed Keedwell
Associate Professor in Computer Science
Skills Development Fellowship Vision
A Skills Development Fellow working with me would develop their understanding of computational and bioinformatics tools for knowledge discovery in biological databases and the optimisation of biological models. The fellow, depending on background, would develop their programming skills, their understanding of key algorithms (e.g. sequence analysis, evolutionary and machine learning algorithms) and the necessary decision making processes to determine which algorithm to apply in which circumstances. Biomedical problems that could be tackled through these approaches would include (but are not limited to) the analysis of genomic and phenotypic data, the analysis of biological sequences, the development of clinical decision support tools and the optimisation of systems biology models.
- 2015 Associate Professor in Computer Science
- 2012 Senior Lecturer in Computer Science
- 2009 Lecturer in Computer Science
- 2006 Temporary Lecturer in Computer Science
- 2003 PhD Computer Science
My research interests are around the development of optimisation and machine learning tools.
Optimisation techniques: evolutionary algorithms, ant colony optimisation, particle swarm optimisation, hyperheuristics, multi-objective approaches.
Machine learning: neural networks and deep learning, random forests, optimisation-ML hybrids (e.g. ECML).
- Sapin E, Keedwell E, Frayling T. (2015) An Ant Colony Optimization and Tabu List Approach to the Detection of Gene-Gene Interactions in Genome-Wide Association Studies [Research Frontier], IEEE Computational Intelligence Magazine, 10:4
- Butt E, Foster JAH, Keedwell E, Bell JEA, Titball RW, Bhangu A, Michell SL, Sheridan R. (2013) Derivation and validation of a simple, accurate and robust prediction rule for risk of mortality in patients with Clostridium difficile infection, BMC Infectious Diseases, 13:1
- JT, Keedwell EK, Frayling TM, Perry JR. (2011) Ant colony optimisation to identify genetic variant association with type 2 diabetes, Information Sciences, 181:9, 1609-1622
Ongoing Projects & Grants
Current EPSRC support focuses on interactive evolutionary algorithm methods: (EP/P009441/1 Human-Computer Optimisation for Water Systems Planning and Management).
Previous EPSRC support has investigated the development of hyperheuristic algorithms for real world problems: (EP/K000519/1 SEQuence-Analysis Based Hyperheuristics (SEQAH) for Real-World Optimisation Problems) and swarm intelligence approaches to problems in bioinformatics: (EP/J007439/1 Ant Colony Optimisation for the Discovery of Gene-Gene Interactions in Genome-Wide Association Studies)
- Prof Tim Frayling and Dr Andrew Wood (University of Exeter Medical School)
- Dr Steve Michell and Prof. Rick Titball (University of Exeter College of Life and Environmental Sciences)
- Prof Stephen Smith (University of York)
Research Group Connections