Jun Zhang

Prof. Zhang develops algebraic and geometric methods for data analysis. Algebraic methods are based on theories of topology and partially ordered sets (in particular lattice theory); an example being formal concept analysis (FCA). Geometric methods include Information Geometry, which studies the manifold of probability density functions. He interests include 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.