Minh-ngoc Tran

Biography

Minh-Ngoc’s main research interests lie in Bayesian methodology and statistical machine learning. He specialises in fast Variational Bayes and simulation-based methods, such as importance sampling and sequential Monte Carlo, for estimating complex models with Big Data, and in Lasso-type variable selection methods. His current research is focused on developing efficient methods for estimating statistical models with an intractable likelihood, of which Big Data problems and Approximate Bayesian Computation are special cases. Minh Ngoc received a PhD in Statistics from the National University of Singapore, a Master and a Bachelor in Mathematics from the Vietnam National University, Hanoi. Before joining the University of Sydney, he worked as a postdoctoral fellow at the University of New South Wales. He is an Associate Investigator in the ARC’s Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).

Research Interest

Statistical inference with intractable likelihood and big data Variational Bayes Bayesian statistics Monte Carlo methods Statistical machine learning Copula modelling and flexible modelling