Research projects in Information Technology

There are sometimes emerging prolonged periods of highly persistent evolution in time series.

Species’ distributions are shifting in response to global climate change and other human pressures. Accurate methods to monitor and predict distribution shifts are urgently needed to manage threatened species and ecosystems, and to control invasive species and diseases. This requires a step-change in the data and methods used to monitor and predict organism behaviours and ultimately shifts in species' distributions.

This PhD project is funded by a successful ARC Discovery Project grant: "Improving human reasoning with causal Bayesian networks: a user-centric, multimodal, interactive approach" and the successful applicant will work as part of a larger research team.

Reasoning, constraint solving and optimisation technologies have made remarkable progress over the last two decades. A number of formalisms like Boolean satisfiability (SAT), satisfiability modulo theories (SMT) and their optimisation extensions (MaxSAT and MaxSMT) as well as constraint programming and optimisation (CP) and mixed-integer linear programming (MILP) can be seen as success stories in computer science.

Time series are an ever growing form of data, generated by numerous types of sensors and automated processes. However, machine learning and deep learning methods for analysing time series are much less advanced than for other forms of data.

Our research is revolutionising the analysis of time series data. But it is early days, and many more impactful challenges are yet to be overcome.

This project is funded by the Australian Research Council and will be conducted as part of a large world-leading research team.

This project works with leading researchers in the Faculty of Pharmacy to develop new artificial intelligence technologies to aide discovery of drugs to treat pharmacoresistant epilepsy.

You can find some of our publications here: https://giwebb.com/research/computational-biology/

This project concerns the investigation of suitable socio-technical data infrastructure for law-enforcement research and development. International collaboration between law-enforcement agencies, research institutions, and commercial organisations is vital to address the large scale technical challenges inherent in combating criminal network activity.  A significant issue in this work concerns the data infrastructure necessary for collaborative research into, and development of, analytical techniques and algorithmic models.

Networked digital diagnostic, monitoring and patient treatment tools permeate medical practice. A plethora of telemedicine, national and other eHealth records, injury assessment, patient-specific devices, hospital theatre equipment tools have resulted in a multi-billion dollar industry worldwide.  Research suggests the application of these tools  to healthcare can improve clinical workflows and patient care outcomes.

Computational simulations are now widely employed to study the behaviour of social systems, examples being market behaviours, and social media population behaviours. These methods rely heavily on game theoretical modelling, usually employing populations of software agents to emulate the behaviour of human populations. Researchers construct models, usually based on known games, and empirical data, and use these to explore how the population reacts to changes. Many behaviours that are not well understood in social systems can be accurately captured and understood using these techniques.

Agent-based computational simulations are now widely employed to study the evolution of behaviour, e.g., predator-prey simulations, the evolution of cooperation and altruism, the evolution of niches and food chains. These methods implement evolutionary processes in virtual populations of software agents and explore the evolution of their behaviour in diverse environments. Many behaviours that are not well understood in biological systems, that are difficult or impossible to measure in real environments, can be accurately captured and understood using these techniques.