About me

I am a PhD student at the Technical University of Munich (TUM) working in the Learning Systems and Robotics Lab (LSY) advised by Prof. Angela Schoellig. My research aims to enable robots to execute complex tasks in uncertain, dynamic environments. For this, I am investigating the use of deep generative models like diffusion models to learn policies for vision-based planning and control.

Before joining LSY as a PhD student, I obtained a Master’s degree in Electrical and Computer Engineering at TUM in 2023 and a Bachelor’s degree in Mechatronics at FAU Erlangen-Nuremberg in 2020,funded by the German Academic Scholarship Foundation. During my studies, I spent a semester at EPFL and conducted research in machine learning for robotic throwing at the Learning Algorithms and Systems Laboratory (LASA) advised by Prof. Aude Billard. Previously, I also worked at Prof. Sandra Hirche’s Chair of Information-Oriented Control at TUM and at Prof. Knut Graichen’s Chair of Automatic Control at FAU. In 2021, I did a research internship in optimal control for autonomous driving at Bosch Corporate Research in Renningen, Germany.

Student Supervision I am open to supervising ambitious and talented Master’s and Bachelor’s students for their thesis. If you want to work with me, please send me an email describing your area of interest. Please also attach your CV and up-to-date transcripts.


05/24I have presented our RA-L paper and a workshop paper “Safe Offline Reinforcement Learning using Trajectory-level Diffusion Models” at the International Conference on Robotics and Automation (ICRA) in Yokohama, Japan. Check out the workshop paper here!
01/24Our paper “Is Data All That Matters? The Role of Control Frequency for Learning-Based Sampled-Data Control of Uncertain Systems” has been accepted at the American Control Conference (ACC). You can find the paper here!
12/24I have joined the Learning Systems and Robotics Lab at TUM as a PhD student, advised by Prof. Angela Schoellig.
10/24Our paper “Vision-Based Uncertainty-Aware Motion Planning Based on Probabilistic Semantic Segmentation” has been published in the Robotics and Automation Letters (RA-L). Check out the paper here!