Learning and Optimisation


The group aims to explore new methods and algorithms for collaborative learning and optimization to achieve synergistic effects. We also seek to promote seamless integration of learning and optimization techniques with real-time systems, cyber-physical systems, autonomous robotics, as well as  process control and automation.

Our methodological research concerns: metaheuristics for learning, data driven learning in optimization, real-time learning, distributed learning, data reduction and feature mining, as well as  reasoning under uncertainty.

We are also actively engaged in practical applications, to  apply the new developed methods and algorithms to solve  challenging problems in industrial and medical domains. 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) and anomaly detection
  • Complex data analysis in biofeedback systems
  • Process automation and networked control systems
  • Behavior learning and control for autonomous robots
  • Cyber security and safety analysis

Ning Xiong, Professor

Email: ning.xiong@mdh.se
Room: U1-126
Phone: +46-21-151716