My research is on the intersection between theoretical quantum many-body physics and machine learning: I apply machine learning as a computational tool to solve problems in quantum physics, and I use approaches from theoretical physics to understand deep learning. My goal is to develop a theoretical foundation of deep learning which would in turn allow to expand its applicability in physics. I am particularly interested in approaches involving information theory, statistical learning theory, and statistical physics. Several of my recent projects on the machine-learning side focus on adversarial attacks and model robustness.
Previously, I completed the Perimeter Scholars International master's program (2017), a MSc in Theoretical Physics with focus on computational approaches to quantum many-body systems (2016), and a BSc in Biophysics (2014) at the Goethe University in Frankfurt, Germany.