معرفی
Prasenjit Ghosh is an Instructional Assistant Professor in the Department of Statistics at Texas A&M University, affiliated with the College of Arts & Sciences. His research focuses on Bayesian Modeling, Multivariate and Functional Data Analysis, and Statistical Learning with applications in Machine Learning. He holds a primary appointment in the Department of Statistics and maintains an active research program in high-dimensional data methodologies.
His research interests emphasize Bayesian inference techniques for complex data structures, including shrinkage priors, graphical models, and stochastic block modeling. He explores methodological advancements in variable selection, posterior contraction rates, and asymptotic properties of Bayesian procedures under sparsity conditions.
Recent publications highlight contributions to global-local shrinkage priors, covariate-dependent graphical models, and step-down procedures for simultaneous hypothesis testing. His work bridges theoretical statistics with practical applications in machine learning and high-dimensional analysis.
No scientific awards or grants are explicitly listed in the provided materials. No advising relationships or laboratory affiliations were noted in the text.
