Optimization and Control
The division has a broad research activity in the general area of control and optimization. We are developing fundamental theory for dynamical systems and are constantly attempting to push the boundaries for advanced control strategies such as networked, hybrid and asynchronous control.
A significant effort is devoted to developing algorithms distributed optimization, which enable efficient coordination of large engineering systems towards their optimal operation. A common theme in much of our recent research is data-driven decision-making: we are developing advanced machine-learning algorithms for harnessing the power of streaming data flows, striking a delicate balance between exploration and learning, and exploiting the acquired knowledge to make optimal decisions.