My research interests are broadly in AI & machine learning with a particular focus on graph machine learning, which includes a class of challenging problems that can be naturally characterized using relational structures. The goal is to more efficiently and reliably learn from relational patterns and reason over them. This is a highly interactive field, where techniques from machine learning (e.g., deep learning, graph representation learning, probabilistic methods), knowledge representation (e.g., logical reasoning), and theoretical computer science (complexity theory, graph theory) are relevant. These methods are applied in a wide range of domains, ranging from systems in life-sciences (e.g., physical, chemical, and biological systems) to social networks.