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Our lab seeks ingredients for intelligence. We use advances in machine intelligence to understand human intelligence and apply insights from human intelligence to develop fruitful kinds of machine intelligence. Our focus is on human cognitive abilities that elude AI systems. Recent projects include few-shot learning of new concepts, learning to generate new goals, learning to ask questions, and learning to produce novel combinations of known components. Our technical efforts focus on modern neural network modeling, meta-learning, fine-tuning large language models, neuro-symbolic modeling, and learning from child headcam videos to see the world through their eyes and ears. By exploring what makes human intelligence unique, we aim to advance both psychology and computer science. Cognitive psychology studies what people can do that current models cannot and do not fully understand, while in computer science, human abilities represent open engineering challenges that can be addressed by reverse-engineering human solutions. My work has uncovered key cognitive ingredients and the inductive biases that are missing from current models. Furthermore, we have demonstrated that by incorporating these ingredients, AI systems can achieve powerful human-like capabilities, thereby addressing longstanding debates regarding the capabilities of neural networks and the essential ingredients for learning.
GRE scores are not accepted. Ph.D. is the primary degree; students are not required to hold an M.S.E. prior to admission.