My research is in the area of hybrid intelligent systems and their application for engineering problems. My research interests cover sub-symbolic approaches to machine intelligence, with special emphasis on learning and decision support. In particular, I have been working on computational learning systems and simulated evolutionary systems including support vector machines, decision trees, neural computing, evolutionary algorithms and fuzzy systems, and the application of these techniques to real world problems, mainly in the area of scheduling, optimization and decision support.

In recent years, my research has focused on the development of adaptive intelligent system methods for large complex engineered systems, such as the electric power system and urban transportation systems. Adaptive and intelligent systems are cutting-edge systems which aim to mimic human reasoning and, to some extent, the processes decision making in general. These systems are examined in various projects by applying multidisciplinary methods that are able to cope with the problems of imprecision, learning, uncertainty and optimization, when concrete models are constructed. In particular, the work in the last one year has involved: