Andrew is a PhD student in cognitive science at UCLA, studying human reasoning, learning, conceptual representations, and adjacent phenomena in machines. His interests vary broadly and include the impact of perception in cognition, but generally center on the question of what sorts of computational principles underlie the flexibility and generalizability of human thought. He currently focuses on how people learn abstract concepts, or concepts that are not tied down to any particular perceptual manifestation, and builds machine learning models with the ability to abstract and reason relationally. An overarching goal is to imbue machines with abilities that are both more human-like and interpretable, in an effort to move past the rigid and black box nature of current deep learning approaches. His primary goal is one of basic psychology: to uncover the workings of the human mind. Andrew's background is in cognitive science and philosophy. He attended Williams College and Oxford University.