Prof. Wing H. Wong gave a lecture named “Multivariate Density Estimation” at AMSS on June 4, 2012.

In his talk, he introduced some recent progress on nonparametric density estimation. He examined how to construct density approximation classes by sequential partitioning. Bayesian density estimation based on such classes would be developed and the associated computational issues would be analyzed. Finally, the approach would be extended to the survival analysis setting.

Professor Wing H. Wong is a professor of Statistics and of Health Research and Policy in Stanford University. Prof. Wong is a mathematician who has helped advance statistical theory related to partial likelihood and sieve convergence. His data augmentation concept helped introduce Markov chain Monte Carlo methods into Bayesian statistics, and he developed statistical methods to analyze data from genomics experiments.  Prof. Wong’s current research interests is to develop and enhance tools in exploratory data analysis, multivariate analysis, information theory, machine learning, Monte Carlo, graph theory, linear and nonlinear differential equations, and applies them to problems in computational and systems biology.

Prof. Wong received many awards for his great achievements in science. In 1993, he received the COPSS Presidents' Award, which is awarded by the five main statistical societies to a person under the age of 40, in recognition of outstanding contributions to the profession of statistics. He was elected to the National Academy of Sciences of the United States of America in 2009.