My research focuses on studying features of machine learning models used for the prediction of atomistic properties. This allows us to better understand the internals of these kinds of models and thereby inspiring new developments. I am passionate about developing software that helps researchers to push the boundaries of materials science research. In my free time, I enjoy tennis and running outdoors. In addition to my research, I am also skilled in programming languages such as Python and C and am interested in diving more into Rust and Scala. I have experience managing high-performance computing systems and have contributed to several open-source software projects in the field of computational materials science. I am always looking for opportunities to collaborate with others and learn from their experiences.