Research Interests

• Learning-Based Control – Integrating methods from machine learning and control theory to develop data-driven controllers for complex dynamical systems.
• Uncertainty Quantification and Robustness – Developing techniques to reason about uncertainty and enable safe and reliable decision-making, with a focus on Bayesian approaches and Monte Carlo methods.
• Learning-Based Control of Unknown Systems with Latent States – Designing algorithms for joint state and dynamics estimation, as well as optimal control, in the absence of full state measurements using Bayesian state-space models and (particle) Markov chain Monte Carlo methods to infer analytically intractable distributions.
• Active Learning for Dynamical Systems – Creating strategies that efficiently explore and identify system dynamics from limited data, guided by model uncertainty.

Research Project

I am currently involved in the ONE MUNICH Strategy Forum Project - Next generation Human-Centered Robotics.

Student Projects and Theses

I am constantly looking for motivated students who are interested in my field of research. Please contact me via e-mail if you are interested in working on a thesis (e.g., bachelor or master thesis) under my supervision, even if no open topics are currently listed.

Please include your preferred starting date as well as your CV and transcript of records in your e-mail. This helps me to select a topic matching your background.