How do we learn to make sense of the world?

At birth, brains face a complex computational challenge. Without prior experience or instruction, brains must convert streams of high-dimensional sensory input into knowledge and adaptive action. I am interested in the algorithmic principles underlying this feat. What learning mechanisms drive the development of perception and cognition? What role does experience play in the development of knowledge? How does intrinsic curiosity scaffold learning? 

To address these questions, I work at the intersection of cognitive science and artificial intelligence (AI).

Using tools from AI, I formalize theories from psychology as agent-based models that use deep neural networks to perform the same tasks as humans and animals. Specifically, I situate computational neuroscience models in virtual animal bodies to test how different components of embodied systems (e.g., neural mechanisms, the environment, body morphologies) drive the behavior of an entire organism. By simulating the development of intelligence with AI, I aim to build predictive, integrative, and embodied models for psychology and neuroscience.