Wing Hung Wong

My statistical research has been focused on the inference of high- or infinite-dimensional parameters from large data sets. Main theoretical results include the characterization of the large sample behavior of the likelihood function in terms of the metric property of the parameter space, and the construction prior distributions that allow practical nonparametric inference in high dimension. To implement statistical modeling and inference computationally I have worked on various aspects of Monte Carlo simulations and some of these works had helped to introduce Markov chain Monte Carlo methods into Bayesian statistics. On the application front, I have been involved in the development of statistical tools to analyze data from genomics experiments such as gene expression microarrays. My current biological interests include gene regulatory analysis in embryonic stem cells and transcript isoform inference from RNA-sequencing data.