UoA 11 Computer Science and Informatics

Our Computer Science research focuses on three main areas:

  • Artificial Intelligence
  • Machine Learning Evolutionary Computation and Optimisation
  • Knowledge Representation

Research takes place primarily within the multi-disciplinary College of Engineering, Mathematics and Physical Sciences with one member of the group working in the College of Life and Environmental Sciences.

Much of our work is interdisciplinary and we collaborate with scientists working across the University of Exeter’s  .

Computer Science, with a strong group working on biological problems and bio-inspired computing, contributes particularly to the Systems Biology theme.

We have a clear focus on artificial intelligence and machine learning, with strong sub-groups focused on evolutionary optimisation and biological problems.

Research in this area will also benefit from the new £52.5million Living Systems Building due for completion in 2016.

Key results

  • 23 per cent of this research was ranked as world-leading (4*).
  • This unit was ranked 26 out of 89 nationally.

Impact case study

NameSummary
Ensuring air traffic safety: optimising short-term conflict alert systems Short Term Conflict Alert systems are used by NATS to alert air traffic controllers to the risk of aircraft becoming dangerously close. This research has provided the means to enhance international air traffic safety by automatically optimising STCA systems so as to simultaneously maximise the number of alerts raised in response to truly dangerous situations, while, at the same time, minimising the number of false alerts. This has been achieved by developing multi-objective evolutionary algorithms to automatically locate the Pareto front describing the optimal trade-off between the numbers of true and false positives. The optimiser is described by NATS as “an outstanding improvement to our safety” in the project report.

Research groups

GroupAbout the Group
High Performance and Scientific Computing

The group's work will complement our current research in big data, optimisation and bioinformatics, as well as giving new tools for physics, engineering and social sciences

High Performance and Scientific Computing is essential to the  ,   and Exosolar Planets themes of the university’s Science Strategy. We will also build on a current collaboration with the Met Office.

UoA 11 Computer Science and Informatics

Our Computer Science research focuses on three main areas:

  • Artificial Intelligence
  • Machine Learning Evolutionary Computation and Optimisation
  • Knowledge Representation

Research takes place primarily within the multi-disciplinary College of Engineering, Mathematics and Physical Sciences with one member of the group working in the College of Life and Environmental Sciences.

Much of our work is interdisciplinary and we collaborate with scientists working across the University of Exeter’s  .

Computer Science, with a strong group working on biological problems and bio-inspired computing, contributes particularly to the Systems Biology theme.

We have a clear focus on artificial intelligence and machine learning, with strong sub-groups focused on evolutionary optimisation and biological problems.

Research in this area will also benefit from the new £52.5million Living Systems Building due for completion in 2016.

Key results

  • 23 per cent of this research was ranked as world-leading (4*).
  • This unit was ranked 26 out of 89 nationally.

Impact case study

NameSummary
Ensuring air traffic safety: optimising short-term conflict alert systems Short Term Conflict Alert systems are used by NATS to alert air traffic controllers to the risk of aircraft becoming dangerously close. This research has provided the means to enhance international air traffic safety by automatically optimising STCA systems so as to simultaneously maximise the number of alerts raised in response to truly dangerous situations, while, at the same time, minimising the number of false alerts. This has been achieved by developing multi-objective evolutionary algorithms to automatically locate the Pareto front describing the optimal trade-off between the numbers of true and false positives. The optimiser is described by NATS as “an outstanding improvement to our safety” in the project report.

Research groups

GroupAbout the Group
High Performance and Scientific Computing

The group's work will complement our current research in big data, optimisation and bioinformatics, as well as giving new tools for physics, engineering and social sciences

High Performance and Scientific Computing is essential to the  ,   and Exosolar Planets themes of the university’s Science Strategy. We will also build on a current collaboration with the Met Office.