My research explores how human intelligence reflects the structure and statistics of natural and artificial curricula, and how this insight can inform the development of artificial intelligence. While computational neuroscience and machine learning often focus on identifying architectural inductive biases that influence learning, my goal is to identify algorithmic inductive biases that are shaped by data. I'm particularly interested in understanding the curricular factors that enable symbolic reasoning and abstraction in neural networks.