neural computation, multi-agent artificial life systems, evolutionary computation, cellular automata models of self-replication, knowledge acquisition, abductive reasoning, Bayesian classification and networks, parsimonious covering theory.
James Reggia received his PhD in Computer Science from the University of
Maryland in 1981. He is currently a Professor in the Department of Computer
Science with a joint appointment in the Institute for Advanced Computer
His research interests span swarm intelligence, neural computation, AI, genetic programming, and artificial life. In the area of swarm intelligence, his work has focused on developing new methods for controlling collective movements and problem solving, studying how individual agent memory abilities influence collective problem solving, and applying swarm intelligence to self-assembly tasks. Recent work in the area of neural computation has focused on developing new learning methods for use in recurrent networks, generating symbolic interpretations of what trained networks have learned, and constructing large, brain-inspired models of cognitive control. This past work has been funded by DARPA, ONR, NSF, NIH, NASA, and multiple industry sources. He regularly teaches computer science courses in neural computation, AI, machine learning, evolutionary computation, and artificial life.
LinksNeuroscience and Cognitive Science Program
Applied Mathematics and Scientific Computation