Discovery Early Career Researcher Award - Grant ID: DE230101025
Funder
Australian Research Council
Funding Amount
$456,106.00
Summary
Food for thought: identifying dietary influences on decision making. Cues that signal food are abundant in the surrounding environment, yet their ability to stimulate food consumption remains poorly understood. This project seeks to identify how food cues influence decision-making processes in the presence of food cues. It will also test how dietary habits alter responding to food cues, and explore the underlying neural mechanisms of these effects. Sophisticated behavioural neuroscience techniqu ....Food for thought: identifying dietary influences on decision making. Cues that signal food are abundant in the surrounding environment, yet their ability to stimulate food consumption remains poorly understood. This project seeks to identify how food cues influence decision-making processes in the presence of food cues. It will also test how dietary habits alter responding to food cues, and explore the underlying neural mechanisms of these effects. Sophisticated behavioural neuroscience techniques will be employed in a validated rodent model of the modern diet. Expected outcomes include new interdisciplinary knowledge identifying how nutritional choices influence cognition and the brain. The project should inform how the modern environment shapes dietary habits.Read moreRead less
A safety-preserving ecosystem for autonomous driving. In this project, Macquarie University will collaborate with UTS and SilverQuest to develop an innovative safety-preserving ecosystem for autonomous driving. This system will not only be adopted by SilverQuest’s customers (automotive companies) to secure their latest autonomous driving models, but also be commercialised as a toolset that can be plugged into existing autonomous vehicles to detect and prevent malicious attacks on autonomous driv ....A safety-preserving ecosystem for autonomous driving. In this project, Macquarie University will collaborate with UTS and SilverQuest to develop an innovative safety-preserving ecosystem for autonomous driving. This system will not only be adopted by SilverQuest’s customers (automotive companies) to secure their latest autonomous driving models, but also be commercialised as a toolset that can be plugged into existing autonomous vehicles to detect and prevent malicious attacks on autonomous driving models. The project will lead to two innovations: in theory design an attack detection and prevention ecosystem for autonomous driving and in application implement a safety analysis toolset for industry-scale autonomous systems.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101310
Funder
Australian Research Council
Funding Amount
$426,918.00
Summary
Dimension-reduced Reinforcement Learning for Large-scale Fleet Management. This project aims to address the problems in large-scale fleet management to ensure the efficiency of tomorrow’s transportation models, such as on-demand ride-hailing and mobility-as-a-service. The expected outcomes of this project include improved techniques for optimising the utility of large fleets of vehicles, and particularly robust dimension-reduced reinforcement learning algorithms that are capable of handling the ....Dimension-reduced Reinforcement Learning for Large-scale Fleet Management. This project aims to address the problems in large-scale fleet management to ensure the efficiency of tomorrow’s transportation models, such as on-demand ride-hailing and mobility-as-a-service. The expected outcomes of this project include improved techniques for optimising the utility of large fleets of vehicles, and particularly robust dimension-reduced reinforcement learning algorithms that are capable of handling the complex dynamics of supply and demand in transportation. The results should advance both research and technology in academia and the transportation industry and will also provide significant benefits to Australia and the international community by enhancing the energy-efficiency of and access to the mobility of the future.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100755
Funder
Australian Research Council
Funding Amount
$462,948.00
Summary
Developing phytosystems for the biofiltration of air pollutants . This project aims to develop, evaluate and apply a range of biotechnology driven solutions for the use of phytosystem biofilters designed for air purification. The findings of the project will demonstrate the fundamental mechanisms behind botanical air pollutant biofiltration, apply systematic technological development against a range of air pollutants, and provide strategies to deploy the technology. With a transdisciplinary appr ....Developing phytosystems for the biofiltration of air pollutants . This project aims to develop, evaluate and apply a range of biotechnology driven solutions for the use of phytosystem biofilters designed for air purification. The findings of the project will demonstrate the fundamental mechanisms behind botanical air pollutant biofiltration, apply systematic technological development against a range of air pollutants, and provide strategies to deploy the technology. With a transdisciplinary approach utilising techniques new to this discipline, the project will substantially advance the fundamental science underlying this novel and highly valuable area of air-bioremediation technology, and will create a much stronger economic driver for this Australia-led innovation.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100274
Funder
Australian Research Council
Funding Amount
$415,675.00
Summary
Graph Neural Networks for Efficient Decision-making towards Future Grids. This project aims to develop a breakthrough framework for decision-focused learning by integrating explainable graph neural networks and efficient computational methods. It expects to create new methodologies of graph representation learning for unlocking data insight with spatiotemporal knowledge while to build new accelerated optimisation theories for speeding up decision-focused learning model. The expected outcomes wil ....Graph Neural Networks for Efficient Decision-making towards Future Grids. This project aims to develop a breakthrough framework for decision-focused learning by integrating explainable graph neural networks and efficient computational methods. It expects to create new methodologies of graph representation learning for unlocking data insight with spatiotemporal knowledge while to build new accelerated optimisation theories for speeding up decision-focused learning model. The expected outcomes will advance big spatiotemporal data analytics and nonlinear optimisation theory for solving decision-making tasks towards a future energy system. This should promote the Australian power industry transition to a sustainable future grid based on a digitalisation approach to efficient energy management against climate changes.Read moreRead less
Real-time imaging of crystal strengthening mechanisms in metals. The strength limit of a metal is marked by rapid motion of crystalline defects. The associated speeds can locally approach that of sound. To probe the associated mechanisms clearly requires both spatial and temporal resolution. We propose to create a new bulk x-ray technique with an unprecedented combination of temporal and spatial resolution. We plan to exploit the technique to mediate a step change in modelling strength based on ....Real-time imaging of crystal strengthening mechanisms in metals. The strength limit of a metal is marked by rapid motion of crystalline defects. The associated speeds can locally approach that of sound. To probe the associated mechanisms clearly requires both spatial and temporal resolution. We propose to create a new bulk x-ray technique with an unprecedented combination of temporal and spatial resolution. We plan to exploit the technique to mediate a step change in modelling strength based on twinning. The formation of crystalline twins is known to dictate the strength of the light metal magnesium. A fuller understanding of the effect of twinning on strength in this metal will provide much needed confidence to implement it more widely in energy saving applications.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100245
Funder
Australian Research Council
Funding Amount
$410,518.00
Summary
Bayesian nonparametric learning for practical sequential decision making. This project aims to develop new methods to support practical sequential decision making under uncertainty. It expects to pave the way for the next generation of sequential decision making uniquely characterised by uncertainty modelling, high sample-efficiency, efficient environment change adaptation, and automatical reward function learning. The expected outcomes will advance machine learning knowledge with a new deep lea ....Bayesian nonparametric learning for practical sequential decision making. This project aims to develop new methods to support practical sequential decision making under uncertainty. It expects to pave the way for the next generation of sequential decision making uniquely characterised by uncertainty modelling, high sample-efficiency, efficient environment change adaptation, and automatical reward function learning. The expected outcomes will advance machine learning knowledge with a new deep learning schema for data modelling and sequential decision-making knowledge with a novel deep reinforcement learning methodology. These developments have immediate applications in autonomous vehicles, advanced manufacturing, and dynamic pricing, with scientific, economic, and social benefits for Australia and the world.Read moreRead less
When caring ends: Understanding and supporting informal care trajectories. This project aims to advance understandings of how, why, when, and for whom caring ends, including the socio-cultural and relational factors that shape experiences before, during, and after caring. Using an innovative, multi-method sociological approach, and foregrounding carers’ voices, this project expects to generate new knowledge on the meaning and experience of care and caring. This project is significant in bringing ....When caring ends: Understanding and supporting informal care trajectories. This project aims to advance understandings of how, why, when, and for whom caring ends, including the socio-cultural and relational factors that shape experiences before, during, and after caring. Using an innovative, multi-method sociological approach, and foregrounding carers’ voices, this project expects to generate new knowledge on the meaning and experience of care and caring. This project is significant in bringing together leading researchers and key carer-focused organisations, spanning service sectors and moving across care relationships, life stages and contexts. Expected outcomes include enhanced service capacity with tangible policy and practice benefits that will enable sustainable and fulfilling informal caring experiences.Read moreRead less