Focus
Bayesian computing
Scalable methodology
Data science
Health statistics
Population statistics
Statistical modelling
Group leaders
Prof. James Brown
Prof. Matt Wand
The UTS Statistics and Data Science group has interests that range from the development of fundamental statistical methods to the application of statistics in such areas as population health, forensics, and law. Much of this work is funded through grants from the Australian Research Council, as well as as well as through Australia's National Health and Medical Research Council (NHMRC) project grants, the Bill and Melinda Gates Foundation, and cooperation with Australian government bodies such as the Australian Bureau of Statistics. A major part of our work involves the development of new methodology to investigate complex problems in the age of Big Data. Specific sub-areas of statistical research include:
- Methodology for Big Data - development of scalable statistical methodology for analysis of high volume/velocity data involving machine learning paradigms;
- Health related statistics - applications of statistical modelling within population health, sports science, and clinical trials;
- Statistical data science - applications of machine learning to generate quantitative information for legal scholars, data wrangling and analysis of medical data;
- Official statistics - use of government surveys combined with census and alternative data sources to produce key population statistics;
- Modelling and simulation of stochastic processes - development of methodology for the modelling and simulation of natural phenomena including applications in biology.