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Most recently, the focus of my research has been the (IM), specifically exploring how IM can be used to shape various aspects of reinforcement learning. This has entailed designing algorithms that autonomously set and pursue goals to learn and do so efficiently via . As part of this work, I have also developed evaluation methods for open-ended learning, which is crucial for understanding the breadth and adaptability of these algorithms in learning a wide range of abilities over time. In addition to the aforementioned, I have broad interests in the fields of representation learning, computational statistics, and computational cognitive science, with a deep curiosity about the mechanisms underlying intelligent behaviour induced by brains and machines.