Research Theme: Informed Decisions

The purpose of data collection and modelling is to learn more about the system and make the best possible decisions about it. Under this theme we develop new decision-making methodologies, and exploit Challenging Data, Multiscale Models, and Enabling Algorithms to make decisions.

Research Theme: Informed Decisions Projects

Helping Save Jaguars with a Virtual Peruvian Jungle

ACEMS Deputy Director Kerrie Mengersen is leading a team of researchers to try to save the jaguar population in Peru. Her project combines the use of statistics, mathematics, and virtual reality technology to help leaders make informed decisions about the threatened species, and the land it depends on.

Safer Drug Screening and Better Simulations: Maths Getting to the Heart of the Matter

More accurately simulating how the heart functions and screening for side-effects of new drugs are just two results that have sprung from a collaboration between Australian and British scientists.

The Function with a $1-Million Bounty on its Head

In 1859, the German mathematician Bernhard Riemann wrote a paper generating consequences that are still echoing almost 160 years later. ACEMS researchers are now adding to the legend by employing the Riemann zeta function in their quest to develop tools to analyse big data sets using analogies with physical systems.

Predicting civil unrest and election outcomes using social media data

This project combines large open datasets from social media with predictive machine learning models to predict social unrest events and monitor trends relevant to elections, both in Australia and abroad. The work is done in collaboration with researchers and software engineers from Data to Decisions CRC.

A functional data approach to Macroscopic fundamental Diagrams

Macroscopic fundamental diagrams (MFDs) describe the relationship between aggregated flow and density in urban road networks

Calibration of stochastic traffic models

A fundamental challenge in constructing Big Models is the question of calibration. Big Models typically have a large number of parameters, which need to be inferred from data in a robust and theoretically justifiable way. Approximate Bayesian Computation is one promising approach to tackle this calibration problem for large complex models.

Flexible models and methods for cognitive model-based decision making

This project pursues breakthroughs which allow important questions of basic and applied science to be addressed using mathematical decision models. Advances are made through an interdisciplinary effort, combining recent developments in econometric and statistical methods, cognitive science, and advances in computing. The outcomes will bring to a new range of questions a proven and powerful approach for investigating psychological effects. The project will begin this expansion effort with investigations of choices about health care and consumer purchases.

How are people modelling dengue fever over small-areas and time?

Dengue fever is one of the most serious health issues in the Asia-Pacific region, affecting more than 100-million people every year. ACEMS Researchers carried out a systematic review of the different spatial and spatio-temporal models that are looked into the disease.

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.

Improving the predictive capabilities of stream health

The goal of this 3-year project is to work with Southeast Queensland Healthy Land & Water to design and implement a model-based monitoring, assessment and reporting framework.

Welcome to the ACEMS Reportal

All contributors should enter data on all of the Centre’s reportable key performance indicators (KPIs).