معرفی
Jun Zhang is a Professor of Psychology and holds a courtesy appointment in the Department of Statistics at the University of Michigan. He earned his Ph.D. in Statistics from the University of California, Berkeley. His research spans interdisciplinary areas, including information geometry, optimal transport theory, machine learning, and cognitive neuroscience. Notably, he explores applications of geometric structures to statistical models and neural systems.
Key research themes include the development of reproducing kernel Banach spaces, entropy deformation theory, and the integration of differential geometry with statistical methodologies. His work often bridges abstract mathematical frameworks with empirical studies in neuroscience, such as modeling neuronal firing rates and analyzing fMRI data in social decision-making contexts.
Zhang’s contributions to Bayesian inference, topological data analysis, and neurocomputational modeling reflect his expertise in both theoretical and applied domains. His recent articles highlight advancements in Kähler geometry for optimal transport, statistical mirror symmetry, and the neural basis of strategic reasoning.
While no specific grants or awards are listed in the provided text, his prolific publication record underscores his influence in these fields. Collaborative projects involve interdisciplinary teams addressing complex problems at the intersection of mathematics, psychology, and statistics.
