Guillaume Crevecoeur is associate professor at Ghent University. With his team, he conducts research at the intersection of system identification, control and machine learning for mechatronic and industrial robotic systems. His goal is to endow physical dynamic systems with improved functionalities and capabilities when interacting with uncertain environments, other systems and humans. He is member of Flanders Make in which he leads the Ghent University activities on sensing, monitoring, control and decision-making.

Guillaume received his Master (June 2004) and PhD (May 2009) in Engineering Physics from Ghent University. The focus of his PhD research was the development of model-based optimization and inverse problem techniques, mainly for neuroscience applications. After obtaining his PhD he became a postdoctoral fellow of the Research Foundation Flanders (FWO) where he put his model-based techniques in a more dynamic context for real-world applications. In the winter 2011 he was a visiting researcher at the Technical University Ilmenau and the Physikalische Technische Bundesanstalt, Berlin, Germany, deepening his knowledge on optimization and inverse problem techniques.

Since his appointment as associate professor (Oct 2014) within the Department of Electromechanical, Systems and Metal Engineering he has been working on the modelling, optimization and control of mechatronic and industrial robotic systems. He teaches numerical optimization, modelling of dynamical systems, mechatronics and robotics. His research team follows a multidisciplinary approach when closing the loop from sensors to actuators in mechatronic systems to improve and unlock their functionalities and capabilities. This, by advancing upon the hardware design, dynamical system models and control algorithms in which the information world works closely together with the physical world. His research is focused on the synergetic treatment of data and physical knowledge, nonlinear control and machine learning. This to improve the efficiency, performance, robustness, autonomy of safety-critical physical dynamic systems.