Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire ....Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire risk and spread, trained and validated on a two-decade satellite fire record. The predictive ability of the model will be tested on the 2019/20 fire season to determine if novel drivers of fire can be identified, and the model itself will be operationalised into a novel short-to-mid term fire risk prediction tool. Read moreRead less
Modelling graph-of-graphs for solving document categorisation problems. Documents in the World Wide Web, such as scientific documents, exhibit a referencing structure as well as being structured objects themselves. This project addresses some inherent limitations of existing modelling techniques in order to improve on the quality of results, and to allow the addressing of some unsolved problems involving documents.
Fractional dynamic models for MRI to probe tissue microstructure. This project aims to develop new mathematical tools for mapping tissue microstructural properties via the use of space-time fractional calculus methods. In magnetic resonance imaging, mathematical models and their parameters play a key role in associating information between images and biology, with the overall aim of producing spatially resolved maps of tissue property variations. However, models which can inform on changes in mi ....Fractional dynamic models for MRI to probe tissue microstructure. This project aims to develop new mathematical tools for mapping tissue microstructural properties via the use of space-time fractional calculus methods. In magnetic resonance imaging, mathematical models and their parameters play a key role in associating information between images and biology, with the overall aim of producing spatially resolved maps of tissue property variations. However, models which can inform on changes in microscale tissue properties are lacking. The tools developed by this project will be used to generate new magnetic resonance image based maps to convey information on tissue microstructure changes in the human brain. Additionally, the mathematical tools developed will be transferable to other applications where diffusion and transport in heterogeneous porous media play a role.Read moreRead less
Using species distribution models to make robust conservation decisions. Species distribution models inform numerous conservation decisions, from planning reserves and managing biological invasions to assessing climate change impacts. While it is often vital to predict where suitable conditions for a species occur, many applications disregard uncertainty, leading to unexpected and potentially unacceptable outcomes. This project aims to provide a definitive guide to using species distribution mod ....Using species distribution models to make robust conservation decisions. Species distribution models inform numerous conservation decisions, from planning reserves and managing biological invasions to assessing climate change impacts. While it is often vital to predict where suitable conditions for a species occur, many applications disregard uncertainty, leading to unexpected and potentially unacceptable outcomes. This project aims to provide a definitive guide to using species distribution models in conservation decision-making by integrating ecological and statistical thinking with decision theory. It seeks to describe how to explore the sources of uncertainty and their impact, develop approaches to reducing uncertainty, and evaluate the effects of uncertainty from the decision viewpoint in order to assist more robust conservation decision making.Read moreRead less
International coalitions for climate change mitigation: the role of carbon market linkages and trade restrictions. This project uses cooperative game theory, implementation theory and agent-based modelling to investigate how coalitions to reduce greenhouse gas emissions could be formed and maintained among countries. Applications include the role of carbon market linkage and trade policy, in countries of the Asia-Pacific region.
Evaluating recurrence as a measure of change in interpersonal dynamics. This project aims to develop an automated conversation analysis system to quantify how communication changes over extended periods of time. It is innovative in proposing to extend the theory and methods of recurrence analysis (a dynamical systems technique) to interacting modalities combining text, audio and video, and to longitudinal analyses. The project is significant in being the first to aim to measure communication dyn ....Evaluating recurrence as a measure of change in interpersonal dynamics. This project aims to develop an automated conversation analysis system to quantify how communication changes over extended periods of time. It is innovative in proposing to extend the theory and methods of recurrence analysis (a dynamical systems technique) to interacting modalities combining text, audio and video, and to longitudinal analyses. The project is significant in being the first to aim to measure communication dynamics over time in the fields of education, health, public discourse and science. It is expected to result in new theories and methods for recurrence analysis validated using real-world data; and to enable new technologies for evaluating professional communication training and communication changes resulting from education or disease progression.Read moreRead less
Enhancing and supporting deliberations within multidisciplinary decision teams. A group’s knowledge and reasoning processes are rarely conducted or recorded systematically; a problem particularly pressing for medical decisions made by multidisciplinary teams. This project will perform the first integration of knowledge management principles with group reasoning process models to create an online environment for enhancing the quality of group decisions and their documentation. The online environm ....Enhancing and supporting deliberations within multidisciplinary decision teams. A group’s knowledge and reasoning processes are rarely conducted or recorded systematically; a problem particularly pressing for medical decisions made by multidisciplinary teams. This project will perform the first integration of knowledge management principles with group reasoning process models to create an online environment for enhancing the quality of group decisions and their documentation. The online environment will help medical teams use documented and non-documented knowledge when more than one medical condition is present and no formal guidelines are applicable. The project advances theories of knowledge in groups and is extendable to any deliberations on complex problems.Read moreRead less
Dynamics and control of complex social networks. This project aims to understand the extent to which a given complex social network can be controlled and how different control mechanisms influence network structure and dynamics. There is a great interest in controlling complex networks including social networks as it might contribute to solving important societal challenges. Using gender imbalance, minority marginalisation, and criminal behaviour as case studies, this project will investigate t ....Dynamics and control of complex social networks. This project aims to understand the extent to which a given complex social network can be controlled and how different control mechanisms influence network structure and dynamics. There is a great interest in controlling complex networks including social networks as it might contribute to solving important societal challenges. Using gender imbalance, minority marginalisation, and criminal behaviour as case studies, this project will investigate the direction of networks controllability and mechanisms that would enable alteration of the network in a desired way. This project will have an impact on our current understanding of network's behaviour, and will contribute to solving a range of societal challenges.Read moreRead less
An intelligent machine modelling assistant for combinatorial optimisation. This project aims to discover key fundamental technologies for automating assistance to non-expert users in the formulation of mathematical models. Through automating the modelling of combinatorial optimization problems, this research will generate new knowledge to address the fundamental challenges of automatic mathematical modelling. This intelligent assistant will enable synthesis of new mathematical models through th ....An intelligent machine modelling assistant for combinatorial optimisation. This project aims to discover key fundamental technologies for automating assistance to non-expert users in the formulation of mathematical models. Through automating the modelling of combinatorial optimization problems, this research will generate new knowledge to address the fundamental challenges of automatic mathematical modelling. This intelligent assistant will enable synthesis of new mathematical models through the utilisation of pioneering natural language processing components and novel custom-made machine-readable knowledge bases. The outcome of this research will broaden access to high-quality models by non-expert workforce and alleviate the shortage of expert mathematicians, bringing significant social and economic benefits.Read moreRead less
Quantification, optimisation, and application of deep uncertainty. This project aims to develop a framework for deep uncertainty quantification. There is currently a fundamental gap between deep learning research and the methods required to quantify and manage uncertainties. The research will propose a novel distribution-free methodology to generate deep predictive uncertainty estimates to avoid the assumptions of existing methods. The quality of estimates will be enhanced by applying an interva ....Quantification, optimisation, and application of deep uncertainty. This project aims to develop a framework for deep uncertainty quantification. There is currently a fundamental gap between deep learning research and the methods required to quantify and manage uncertainties. The research will propose a novel distribution-free methodology to generate deep predictive uncertainty estimates to avoid the assumptions of existing methods. The quality of estimates will be enhanced by applying an interval-based adversarial training step. The project is expected to help data-driven Australian organisations and industries to better quantify and manage forecasting uncertainties. This project will provide them with significant cost savings through better decision making and more robust planning.Read moreRead less