Computer Science

Research overview

Research areas

Areas of current interest include:

• machine learning
• shape representation
• nature-inspired computing
• knowledge representation and ontology
• optimisation

The department brings together Computer Scientists and Mathematicians working on a variety of modern-day, interdisciplinary topics including problems of knowledge representation, machine learning, optimisation, image and signal processing. We have our own Beowulf parallel machine and access to the University's EPSRC-funded IBM supercomputer SP3 system. For detailed information see our research webpages and staff profiles.

In the area of machine learning, pattern analysis and statistical computation, which incorporates close collaboration with the mathematical sciences, we research a range of techniques for learning from data, including statistical and nature-inspired methods. Current and recent projects in this area have focused around application areas ranging from bio-medical imaging, face and speech recognition to air-traffic control and safety-critical software design. A strong research theme in this area is shape-based computer vision and image processing, particularly for image retrieval and object classification.

Research on optimisation methods focuses on techniques for evolutionary optimisation, particularly multi-objective optimisation. This work is currently being exploited in areas such as hydroinformatics, credit-card fraud detections, mobile phone network turning and bioinformatics problems. Closely related to these is research into biologically inspired algorithms, such as neural networks, swarm intelligence and artificial life, together with applications to bioinformatics.

Artificial intelligence research interests focus on spatial and temporal knowledge representation, with applications to geographical information science, and on the philosophical foundations of artificial intelligence and computer science.

In the area of hydroinformatics we are mainly concerned with the design and multi-objective optimisation of water distribution and related networks using iterative learning algorithms and neural networks; genetic algorithms; water systems. This is supported by EPSRC, EU and British Council contracts. The work involves collaboration with numerous UK and EU universities and industrial companies.