About me

I am a postdoctoral researcher at Technical University Dortmund, where I work on data-driven modelling of biophysical systems — particularly focusing on cell mechanics and cell motion. My research combines physics-based insight with modern computational approaches to understand complex biological processes.

I earned my PhD in Biophysics from the University of Cologne, specialising in advanced data analysis. During my doctoral studies, I explored innovative methods for traction force microscopy and the data-driven discovery of physical laws.

After completing my PhD, I joined Helmholtz-Zentrum Hereon as a postdoctoral researcher, developing symmetrical and physically constrained neural models for solving partial differential equations. Later, at Ludwig Maximilian University of Munich, I investigated stochastic automatic differentiation techniques for Monte Carlo processes.

My current research interests revolve around the quantification of uncertainty in inverse ill-posed problems — especially in image reconstruction and the identification of governing equations (ODEs, PDEs, and SDEs). To tackle these challenges, I integrate concepts from deep learning, Bayesian inference, optimisation algorithms, and information theory.

Beyond research, I am passionate about interdisciplinary collaboration and advancing reproducible computational science.