At the interface of machine learning and theoretical neuroscience, my research focuses on (i) reverse-engineering in-context learning in transformer models, (ii) making reinforcement learning more sample efficient by introducing model based credit assignment techniques for learning policies grounded in causality and (iii) creating new theories for learning in the brain by bridging optimal control with learning, leading to novel learning algorithms for deep and recurrent neural networks that alleviate important drawbacks of current neural network training methods. My research was awarded several spotlight and oral presentations at the top machine learning conferences, and I have ongoing collaborations with among others João Sacramento (ETH Zurich), Johannes von Oswald (Google Research), Greg Wayne (Google DeepMind), Nathaniel Daw (Princeton), Rafal Bogacz (Oxford) and Angelika Steger (ETH Zurich) on projects involving in-context learning in transformers, model-based credit assignment in reinforcement learning and methods for bayesian learning in the brain.