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
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
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
Understanding the deep driving forces of Earth’s large-scale topography through time. We propose to model the convection of Earth’s mantle linked to tectonic plate motions to unravel their combined influence on the evolution of topography over 550 million years. The project will lead to an understanding of the driving forces of large-scale topography in continental interiors and along their margins through geological time.
Evaluating environment policy that has immediate costs but long-term gains. A fundamental challenge for environmental policies is the different timescales over which ecological and financial costs and benefits occur. For example, whilst revegetation to offset land clearing incurs immediate costs, it can take decades for it to become suitable habitat for wildlife. These long time lags can lead to inefficiencies in spending and poor environmental outcomes. This project aims to develop novel approa ....Evaluating environment policy that has immediate costs but long-term gains. A fundamental challenge for environmental policies is the different timescales over which ecological and financial costs and benefits occur. For example, whilst revegetation to offset land clearing incurs immediate costs, it can take decades for it to become suitable habitat for wildlife. These long time lags can lead to inefficiencies in spending and poor environmental outcomes. This project aims to develop novel approaches for evaluating the future impacts of environmental policies and new methods for improving their design. It is intended that the methods be tested and demonstrated in the policy context of biodiversity offsetting, which is set to play a key role in nature conservation globally.Read moreRead less
Modelling collective behaviour to protect social insect ecosystem services. This project aims to use mathematical models and computer simulations and biological experiments to investigate how social insects adapt to environmental stress, for example due to climate change and pollution. Fundamental to the adaptability of social insects are the complex mechanisms that allow colonies to maintain a carefully balanced division of labour (DOL). This project builds on evolutionary game theory to develo ....Modelling collective behaviour to protect social insect ecosystem services. This project aims to use mathematical models and computer simulations and biological experiments to investigate how social insects adapt to environmental stress, for example due to climate change and pollution. Fundamental to the adaptability of social insects are the complex mechanisms that allow colonies to maintain a carefully balanced division of labour (DOL). This project builds on evolutionary game theory to develop a new approach for analysing how environmental factors impact on DOL and thus colony viability. The project will deliver new methods to assess and predict the impact of environmental stress This will ultimately help to protect these keystones of biodiversity and the significant ecosystem services they provide as pest-control agents, through pollination, seed dispersal, and soil conditioning.Read moreRead less
Large-scale computational modelling of epidemics in Australia. The project aims to develop novel computational epidemiological models to contribute to guidelines for optimal prophylaxis, vaccination and case management. Emerging threats posed by infectious diseases and bioterrorism could have dramatic effects on the Australian population, productivity and economy. The project aims to improve the accuracy and scope of modern computational epidemiological models by integrating large-scale Census d ....Large-scale computational modelling of epidemics in Australia. The project aims to develop novel computational epidemiological models to contribute to guidelines for optimal prophylaxis, vaccination and case management. Emerging threats posed by infectious diseases and bioterrorism could have dramatic effects on the Australian population, productivity and economy. The project aims to improve the accuracy and scope of modern computational epidemiological models by integrating large-scale Census datasets and explicitly simulating the entire population down to the level of single individuals, coupled with complex network-based and information flow analysis. The intended outcomes include a more precise and efficient forecasting of critical epidemic dynamics, and increased effectiveness of prevention, mitigation and management of socio-economic, socio-ecological and national security crises.Read moreRead less
Hierarchical information processing in the primate visual cortex. This project aims to understand how visual information is transformed across hierarchical levels in the brain. Neuroscientists have long recognised that the visual cortex can be conceptualised as a hierarchical processing network. This became apparent when learning algorithms based on hierarchical networks ("deep learning") changed artificial intelligence. This project will combine high-throughput electrophysiology with analytical ....Hierarchical information processing in the primate visual cortex. This project aims to understand how visual information is transformed across hierarchical levels in the brain. Neuroscientists have long recognised that the visual cortex can be conceptualised as a hierarchical processing network. This became apparent when learning algorithms based on hierarchical networks ("deep learning") changed artificial intelligence. This project will combine high-throughput electrophysiology with analytical tools adopted from deep learning. By explaining the physiological properties of higher-level neurons in terms of hierarchical networks, the project expects to address long standing questions in neuroscience, and provide insights on biological hierarchical computation.Read moreRead less