Matthieu Barreau


No open position at the moment.

I am always looking for passionate and talented MSc students for internships or master thesis. Please contact me directly.


I am an Assistant Professor within the division of Decision and Control Systems at KTH, Stockholm, Sweden from September 2023.

I was an algorithm developer/research manager at Tobii and previously a postdoc with Karl Henrik Johansson at KTH. I completed my Ph.D. degree under the supervision of Alexandre Seuret and Frederic Gouaisbaut in LAAS, Toulouse, France.

On this website, you can find a list of my publications with the Matlab/Python/Julia code used for them (if I took the time to upload it correctly on GitHub). For more information feel free to contact me.

Research profile

Optimization of performances and stability analysis of heterogeneous dynamical systems are at the core of my research activities. It translates into an optimization problem under constraints such as model dynamics, stability, saturation, sampling, or delayed control. I actively participate in theory developments and applications in this field.

I am interested in both finding the exact solution to the aforementioned optimization problem, using relaxations to get a semidefinite program, and approximating a solution using machine learning techniques. I then use tools from robust control such as Lyapunov functions and Integral Quadratic Constraints (IQCs) in combination with Physics-Informed Neural Networks (PINNs - see the video on the right). The latter are traditional feedforward neural networks whose output has been enriched by its derivatives with respect to its inputs. This consequently leads to a learning problem under the constraint that the differential equation must hold.

My research expertise and interest are described in the figure above. They consist of three steps and their relations between them. It can be summarized as follows.

These three questions shape the methodology that I will apply in the research program described below. My research is focusing on re-interpretation and coupling between system theory and machine learning. This is conducted in an international environment, mainly with researchers from all over Europe and the US.

Research interests

Physics informed neural networks; traffic systems;

Time-Delay Systems; Infinite dimensional systems;

Lyapunov Methods; Integral Quadratic Constraints;.

Research Gate  -  Google Scholar  -  GitHub


Physics Informed Neural Networks