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

Understanding Rare Events: Simulating Rare Disease Outbreaks and Power Blackouts

Rare events such as the state-wide power blackout in South Australia in September 2016, natural disasters such as floods and bushfires, or the ensuing chaos when parts of a complex interconnected systems such as the internet fail, are difficult for researchers to simulate or model. They're the primary interest of ACEMS Chief Investigator Dirk Kroese.

Anomaly detection in noisy time series

The ability to detect anomalies or outliers in time series, and to develop algorithms that can find them at the earliest possible sign of a deviation from expected behaviour, is critical for many applications including those arising in security, epidemiology, and monitoring of critical infrastructure. This project aims to develop new methods to characterise the expected time series behaviour of systems, even in the presence of significant noise and concept drift, and with spatio-temporal and multivariate time series.

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.

Green Acorn