Jun Zhang is a professor of Psychology and of Mathematics. He is a Fellow of the Association for Psychological Science, and from 2007 to 2008 was President of Society for Mathematical Psychology.
Ph.D. 1992, University of California, Berkeley, California. Neurobiology.
Mathematical psychology and computational neuroscience, broadly defined to include neural network theory and reinforcement learning, dynamical analysis of nervous system (single neuron activity and event-related potential), computational vision, choice-reaction time model, Bayesian decision theory and game theory.
Zhang, J. (2014) “Divergence functions and geometric structures they induce on a manifold.” In F. Nielsen (Ed). Geometric Theory of Information, Springer.
Zhang, J. (2014). “Reference duality and representation duality in information geometry.” In Proceedings of Maximum Entropy and Its Applications 2014.
Ilin, R. and Zhang, J. (2014). “Information fusion with uncertainty modeled on topological event spaces.” Proceedings of IEEE Symposium on Foundation of Computation Intelligence (FOCI 2014).
Ilin, R., Zhang, J., Perlovsky, L., and Kozma, R. (2014). “Vague-to-crisp dynamics of percept formation modeled as operant (selectionist) process.” Cognitive Neurodynamics, 8: 71-80.
Zhang, J. (2013). “Nonparametric information geometry: From divergence function to referential-representational biduality on statistical manifolds.” Entropy, 15: 5384-5418.