Enabling Algorithms

Developments in computing technology have provided the opportunity for the creation of new algorithms to enable improved analysis of data and models. Under this theme we develop the new enabling algorithms required for Challenging Data, the new Multiscale Models and other advances.

Enabling Algorithms Projects

From Cells to Organs: Exploring Variability in Physiological Systems

Everybody is different, and every body is different. Significant variability is a common feature of all of the physiological systems that compose the function of the human body, and understanding this variability is critical to explaining differences in susceptibility to pathological conditions, and also to explaining how medical treatments can potentially succeed or fail even when applied to treat the same condition.

Improving returns from Southern Pine plantations through innovative resource characterisation - virtual log models

Investigation and development of virtual log models for Southern Pines will be based on analysis of data from the cores, peeled billets and approximately 60 sawn logs. We plan to predict log and stem wood properties from the breast height cores taken in the field study. Following this, applied mathematics will be used to investigate the processing of these virtual logs and predict properties for the virtual boards ‘sawn’ from these logs.

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.

Splitting Methods for Optimization and Estimation

Splitting methods involve systems of particles that move around in space and time and have the possibility to die out or split into multiple particles at certain times. Such systems can be used to solve complicated estimation and optimization problems. This project aims to (1) create new and faster splitting algorithms; (2) improve the implementation of splitting algorithms, e.g., via distributed computing; (3) find new applications for optimization and estimation that are well-suited to be solved by splitting methods.

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.

Green Acorn