We have contributed to the session “A Tutorial on Nonlinear Model Predictive Control: What Advances Are On the Horizon?” at the American Control Conference, Atlanta, 2022. The corresponding paper will be published in the conference proceedings and can be found, e.g., here.


I am happy to share that our work on connecting predictive safety filters and control barrier functions “Predictive control barrier functions: Enhanced safety mechanisms for learning-based control” will be published in the IEEE Transactions on Automatic Control in May 2023.


Our paper Learning-based Moving Horizon Estimation through Differentiable Convex Optimization Layers has been accepted for the 4th Annual Learning for Dynamics & Control Conference at Stanford and was selected for oral presentation.


Check out our new pre-print: State space models vs. multi-step predictors in predictive control:
Are state space models complicating safe data-driven designs?


My doctoral thesis has been published, which I successfully defended in September 2021.


Our paper A predictive safety filter for learning-based control of constrained nonlinear dynamical systems was selected as Editor’s Choice Automatica in July 2021



Spotlight talk at IROS 2021 Workshop on Safe Real-World Robot Autonomy: Summarizes some of the more recent work on predictive safety filters.


A predictive safety filter for a miniature racing application and its combination with imitation learning to safely learn an expert policy: Preprint


I presented the concept of predictive safety filters as part of the autonomy talks at ETH. Due to the current pandemic, the talks are available online.

About me

I’m a postdoctoral researcher at the intelligent control systems group at IDSC ETH. My research interests are in the fields of Safe Learning Control, Model Predictive Control, and Model-based Reinforcement Learning, see also my Google Scholar profile.

For a detailed CV, see my LinkedIn profile.