Our research is driven by the goal of using computational modeling, in particular machine learning, to gain insight into the remarkable abilities of the human brain. This computational lens can operate across multiple scales, systems, and species, complementing the specialized, biologically-grounded studies of traditional experimental science. Our group develops machine learning methodology while also studying the statistical principles and theory that can help explain the behavior of the underlying algorithms. As a recent example, we are studying the role of selective attention in abstract reasoning and developing reinforcement learning frameworks that reflect elements of human cognition, memory, and brain organization.
John Lafferty received his PhD in Mathematics from Princeton University, where he was a member of Princeton's Program in Applied and Computational Mathematics. He is Professor of Statistics and Data Science at Yale, with a secondary appointment in Computer Science. He began his career in machine learning at the IBM Thomas J. Watson Research Center in Yorktown Heights, building computational models of human language. Lafferty's WTI service is focused on building a thriving Yale community at the interface of neuroscience and computational modeling.
Shallow neural networks trained to detect collisions recover features of visual loom-selective neuronsNeuroscience (2022)
Surfing: Iterative Optimization Over Incrementally Trained Deep Networks (Advances in Neural Information Processing Systems 32)NeurIPS (2019)