The main motivation for my research is to advance our understanding of how and why deep learning models work. My research toolkit currently focuses around identifiable causal and self-supervised representation learning and out-of-distribution (OOD) generalization, with a focus on compositionality in language models. During my Ph.D., I realized that current machine learning theory is insufficient to explain especially the interesting and useful properties of deep neural networks. I aim to help close this gap, by focusing on: 1) extending machine learning theory to understand the role of inductive biases (e.g., model architecture or optimization algorithm); 2) grounding machine learning in the physical world via (causal) principles and humanity’s prior knowledge; 3) extending our understanding of out-of-distribution and compositional generalization; 4) uncovering overarching patterns across different fields in machine learning. I have done both my M.Sc. and B.Sc. at the Budapest University of Technology in electrical engineering and specialized in control engineering and intelligent systems. In my free time, I enjoy being outdoors and often bring my camera with me.