"Have you heard of industry 4.0, smart grids, smart buildings, smart cities, intelligent traffic systems and intelligent self-​driving cars? Did you ever wonder what makes them smart and intelligent? The answer is control and automation and that’s what we do at the Automatic Control Laboratory (IfA). We use control theory, optimization, machine learning, and game theory to develop controllers and algorithms that are the backbone of nearly all modern technology. At our lab we span the whole area from pure theory to real-​world applications and we are looking for you to help us push forward the state of the art. If you want to learn techniques and gain knowledge that enable you to work in any field from medical applications to spacecraft and from electrical grids to finance then the Automatic Control Laboratory is the place for you!"

Teaching

Linear System Theory - Head teaching assistant (Fall 2020, Fall 2021)

Distributed Systems & Control - Teaching assistant (Spring 2021)

Signals and Systems II - Teaching assistant (Spring 2019, Spring 2020)

Student Projects

I am always looking for talented students with interests in the area of data-driven control, optimization-based learning, networked systems and related topics.
If you would like to apply, please send me an email with your CV and a transcript of records.

Ongoing projects

Plug-and-play Control of Interconnected Systems


Varying-topology networks have attracted the attention of the control systems community due to their broad range of applications, such as microgrids and power networks. In this project we are interested in developing plug-and-play controllers which ensure stability and feasibility for such complex networks.


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Completed projects

Dynamic programming via the randomized mini-batch Gauss-Seidel operator


The Bellman operator constitutes the foundation of dynamic programming algorithms. This project focuses on the study of a practical and theoretically sound extension of the Bellman operator, which we call randomized mini-batch Gauss-Seidel operator.


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Data-driven optimal control via linear programming


Learning how to optimally regulate a dynamical system from data is a fundamental problem in control theory. This project focuses on investigating new theory and methods about the so-called linear programming approach to approximate dynamic programming.


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