Seminars

Since its advent in the early 1990s, functional magnetic resonance imaging (fMRI) has revolutionized cognitive neuroscience by providing a non-invasive…

Tukey’s halfspace depth can be seen as a stochastic program and as such it is not guarded against optimizer’s curse,…

Likelihood method is a key technique in the realm of statistical inference, particularly when dealing with intricate models that encompass…

In this talk, I will introduce two novel nonparametric Bayesian methods for learning spatial homogeneity patterns. Our methods have the…

Motivated by an earthquake impact study, this paper develops new tests with several invariant properties for functional data. For multi-sample…

The centrality in a network is often used to measure nodes’ importance and model network effects on a certain outcome….

Interpretable machine learning has been widely deployed for scientific discoveries and decision-making, while its reliability hinges on the critical role…

In this talk, I will first present a Bayesian semiparametric method of jointly estimating the density function and the tail…

Large crossed mixed effects models with imbalanced structures and missing data pose major computational challenges for standard Bayesian posterior sampling…

Classical machine learning framework focuses on single-stage learning, where we directly estimate the target prediction function from training data sets….