Research Theme: Challenging data

Modern data comes in all sorts of different forms that do not necessarily fit well with traditional data analysis methods. For example, it can occur in the form of images, text, visual displays or mathematical functions. The size of datasets can be much larger than those traditionally analysed and the speed at which decisions have to be made about such data can be much faster than previously required. Under this theme we explore these data sources different types of challenging data and develop new methods to explore such data for its analysis.

Research Theme: Challenging data Projects

Is Growth Velocity associated with Percentage of having Diarrhoea both in first year?

The aim of this project is to investigate the relationship of growth rate and percentage of having diarrhoea (both in the first year of a child’s growth). We have selected fifteen studies. Besides of the two main variables as indicated, we also have their demographic data including some social economic status (SES) information. Both Multiple Regression and Meta-Analysis have been used for the analyses.

Optimising Patient Flow and Throughput in a Surgical Suite.

Demand for healthcare services is growing rapidly in Australia, and rising healthcare expenditure is increasing pressure on the sustainability of the government-funded healthcare system. To keep up with the rising demand, we need to be more efficient in delivering healthcare services. To make a system efficient, we need to identify the source of inefficiency and eliminate it.

Statistical Phylogenetics

In this project we develop the necessary statistical techniques to analyze ancient DNA datasets to address important phylogenetic and population dynamic questions, such as the peopling of Australia and South America, and investigating the genetic diversity of Australia's endemic marsupials.

Stratified Splitting for Efficient Monte Carlo Integration

The project will deliver new theory and methods for fast and robust statistical learning, inference, and parameter estimation in various application fields such as risk management, decision making, health-care, manufacturing, financial engineering, and system reliability. By providing high-quality solutions that are out of reach of the current state of the art, the project will have large-scale impact on operational efficiency of real-life applications in both scientific and industrial domains.

The use of 3 dimensional motion analysis to determine whether quantitative criteria can be found for the Prechtl’s Qualitative Assessment Method of General Movement (GMsA) classifications of writhing and fidgeting in the normative infant population

Prechtl’s Method on the Qualitative Assessment of General Movement (GMsA) of infants (Alexander et al., 1993, Darsaklis et al., 2011, Einspieler, 2004, Haywood and Getchell, 2009, Piek, 2006) is one method of early prediction of neurodevelopmental outcomes in infants. This movement assessment uses video recordings and the naked eye of the assessor and has established 2 distinct movement classifications (Writhing and Fidgeting) which occur in healthy infants aged from term to 1 month and 3 months, respectively.