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.
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
The objective of RECOG is to develop new technology for innovative brain-training for people with cognitive deficits. The ability to focus our attention on relevant information, maintain and manipulate this information during a short period of time (our Working Memory, WM), is central for human cognition.
Project manager at MDH: Elaine Åstrand
Main financing: The Knowledge Foundation