The goal of optimal transport problems, is to find optimal mappings between probability meaures: these mappings are also called transport plans, and can take the form of functional transforms (in Monge's original problem) or joint probability distributions (in the Kantorovitch relaxation).
I am an incoming postdoctoral researcher at INRIA, in the WILLOW team. My work focuses on numerical optimization and optimal control for robotics.
Previously, I did my PhD in the Gepetto team at LAAS-CNRS, and the WILLOW team of Inria Paris, under the supervision of both Nicolas Mansard and Justin Carpentier.
Previously, I obtained an engineering degree from École polytechnique, and a master's degree in applied mathematics and computer science (MVA) from ENS Paris-Saclay.
Introduction to optimal control: LQR
Most control loops out there used in real-world systems are simple feedback loops proportional to the error, its derivative or integral (this is called PID control). However, this kind of control can exhibit undesirable behavior such as oscillations or failing to converge to a given setpoint quickly if at all. Some more complex systems such robots, satellites or cars can come with precise performance requirements, and more carefully constructed control actions need to be supplied with guarantees about their optimality.