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

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.

Plugging the Gaps in Missing Data

Researchers at the ARC Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS) are developing techniques to 'fill in' and use data that hasn't been recorded, to address a range of challenges such as modelling growth patterns of children to the automated interpretation of speech.

Staying True to the Detail in Big Data

This research deals with the field of symbolic data analysis. Researchers have developed ways of estimating how components of the underlying data mix and interact to produce the values of the symbols they observe. So the models they build relate more closely to what's really happening.

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.

A functional data approach to Macroscopic fundamental Diagrams

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

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.

Bayesian Approaches to Modelling and Analysis of Big Data

Bayesian methods for modelling and analysis of data are now well established. However, as with many statistical methods, their applicability in the context ‘big data’ is still being explored. This project will consider four questions that relate to ‘scalable Bayes’, that is, Bayesian models and computational methods that scale up to big data problems.

This project links to a number of other projects across the Centre.

Bayesian variable selection for sparse solutions.

In this project we consider the challenging task of developing fully Bayesian sparse analyses for the situations when the numbers of predictors is larger than observations for complex responses and covariates grouped by blocks with the sparsity for blocks and cases.

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.

Welcome to the ACEMS Reportal

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