Seminars

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….

A growing number of nanomedicines have been developed for drug delivery via injection into the bloodstream. These nanocarriers transport drug…

In statistical and probabilistic models, “mean-field” behavior arises when the aggregate effect of many variables is well-approximated by an average…

In recent years, graphs have become one of the most powerful abstractions for complex data, including brain networks, knowledge graphs,…