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Our lab seeks ingredients of intelligence. We use advances in machine intelligence to understand human intelligence and use insights from human intelligence to develop fruitful kinds of machine intelligence. Our focus on human cognitive abilities eludes AI systems. Recent projects focus on 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 emphasize modern neural network modeling, meta-learning, fine-tuning large language models, neuro-symbolic modeling, and learning from child headcam videos—seeing the world through their 'eyes and ears'. By exploring what makes human intelligence unique, we aim to advance the fields of psychology and computer science. Cognitive psychology reveals capabilities that current models cannot fully understand, while computer science addresses the open engineering challenges posed by human abilities—notably, the reverse-engineering of human solutions. Our work has uncovered key cognitive ingredients and inductive biases that are missing in current models and demonstrated that incorporating these ingredients can make AI systems powerful and human-like, thereby addressing longstanding debates about the capabilities of neural networks and the necessary 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.