Computer and Data Science

Learning and Optimisation

The group aims to explore the synergy between machine learning and optimization to achieve collaborative effects in building highly efficient and smart systems.


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The methodological research concerns: metaheuristics for learning, data driven learning in optimization, real-time learning, data reduction and feature mining, learning and optimization under uncertainty.

We are also actively engaged in practical applications, to test and apply the new developed methods and algorithms in current challenging scenarios such as industrial or biomedical ones. The interesting application areas include (yet are not limited to) the following:

  • Machine learning and optimization in power devices and power systems
  • Real-time process monitoring (both in industry and health care)
  • Complex data analysis in biofeedback systems
  • Process automation and intelligent control systems
  • Behavior learning and control for autonomous robots
  • Cyber attack identification
  • Software testing

Ongoing research projects