My general research interests are probabilistic machine learning and machine learning grounded in causal and statistical principles. My current research focuses on in-context learning for probabilistic and causal inference. This relates to the fields of Amortized Inference, Prior-data Fitted Networks, Simulation-Based Inference, Neural Processes, and Large Language Models.