Daniel Yamins is assistant professor of psychology and computer science at Stanford University, a faculty scholar at the Stanford Neurosciences Institute, and an affiliate of the Stanford Artificial Intelligence Laboratory. His research group focuses on reverse engineering the algorithms of the human brain, both to learn about how our minds work and to build more effective artificial intelligence systems. He is especially interested in how brain circuits for sensory information processing arise via the optimization of high-performing cortical algorithms for key behavioral tasks. Most recently, he has used performance-optimized deep neural networks to build neurophysiologically accurate models of higher visual and auditory cortex. He received his A.B. and Ph.D. degrees from Harvard University, was a postdoctoral research at MIT, and has been a visiting researcher at Princeton University and Los Alamos National Laboratory. He is a recipient of the James S. McDonnell Foundation award in Understanding Human Cognition.
Current Project: Neural computations for visual form processing and form-based cognition