About me
I studied Computational Engineering Science at RWTH Aachen University, Germany, and received my M.Sc. degree in 2016. Afterwards, I began my PhD studies under the supervision of Prof. Stefanie Elgeti and Prof. Marek Behr at the Chair for Computational Analysis of Technical Systems (CATS), RWTH Aachen University. During that time, I had the opportunity to do a research stay in the group of Prof. Gianluigi Rozza at SISSA, International School for Advanced Studies, Trieste, Italy. In autumn 2019, I transferred to the group of Prof. Stefanie Elgeti at the Institute of Lightweight Design and Structural Biomechanics (ILSB), TU Wien, Austria. After receiving my doctoral degree in 2021, I continued my academic work as a post-doc at ILSB.
From 2021 to 2023, I was a member of the GAMM Juniors. In 2022, my dissertation entitled “Advanced Full- and Reduced-Order Simulations as Digital Tools in Production Engineering” was awarded the Dr. Klaus Körper Prize by GAMM. In 2025, I was also nominated by CEACM as a member of the ECCOMAS Young Investigators Committee.
In addition, I am involved in the scientific community, for instance by organizing professional events such as the GACM Colloquium on Computational Mechanics for Young Scientists from Academia and Industry in 2023, which has been certified as a sustainable event, or the CISM course on quantum computing in computational mechanics in 2025.
Research
My research interests lie in the area of developing simulation-based methods as digital tools for solving engineering problems. I am currently exploring novel computational paradigms, particularly applying quantum computing to design optimization. This line of research is advanced through my FWF ESPRIT project, “Lightweight Design Optimization Using Quantum Annealing ,” which I direct as principal investigator.
Previously, my focus has been on developing advanced computational models, such as those based on the space-time finite element method (FEM), and constructing surrogate models using techniques like model order reduction (MOR) or probabilistic machine learning, particularly Gaussian process regression (GPR). The methodologies have been successfully applied to several complex applications relevant to manufacturing engineering and related fields. In addition, these surrogate models have been integrated into optimization or uncertainty quantification (UQ) processes. Furthermore, I participated in a project on learning optimization strategies for recurring design optimization tasks using reinforcement learning (RL).