Rate My Professor Brenda Vo

Dr. Brenda Vo is a Senior Lecturer in Statistics in the School of Science and Technology at the University of New England, where she also serves as Discipline Convenor and Research Coordinator for Statistics. She joined the university in August 2017. Prior to this, she worked as a biostatistician on projects in public health and nutrition sciences at Queensland University of Technology and Menzies School of Health Research. Earlier in her career, she taught mathematics at Kien Giang Community College in Vietnam. Vo earned her PhD in Computational Bayesian Statistics from Queensland University of Technology in 2016. Her thesis developed new statistical methods to estimate parameters of mechanisms driving cell population spread, including motility, proliferation, and cell-to-cell adhesion, quantify uncertainty, and examine influences from various factors, providing tools to assess medical treatments targeting cell spread.

Vo's expertise encompasses theoretical and applied Bayesian statistics, particularly in health and cell biology applications. Her research interests include agent-based models, Approximate Bayesian Computation, multivariate statistical methods, mathematical biology, and health statistics. Ongoing projects feature statistical inferential techniques for agent-based models of Chlamydia infection in guinea pigs, simulations of general practitioner distribution in New South Wales, modeling Q fever notifications linked to livestock movement and factors affecting immunisation rates, and evaluation of the Living Well Multicultural Lifestyle Modification Program. She teaches probability and stochastic modelling, statistical inference, statistical methods in health, and statistical learning, and co-supervises three PhD students on Bayesian methods for ion-selective electrode sensors, ecology and management of vegetable weeds in Australia, and outmigration with agrarian transitions in Myanmar. Key publications comprise 'Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation' (Mathematical Biosciences, 2015), 'Melanoma cell colony expansion parameters revealed by approximate Bayesian computation' (PLoS Computational Biology, 2015), and 'Bayesian parametric bootstrap for models with intractable likelihoods' (Bayesian Analysis, 2018). Vo belongs to the International Society for Bayesian Analysis, Statistical Society of Australia, Q Fever Research Consortium, and NSW Health Technology Assessment Working Party.