Discovery Early Career Researcher Award - Grant ID: DE200100479
Funder
Australian Research Council
Funding Amount
$427,116.00
Summary
A Unified Framework to Rapidly Fabricate Individualised Activity Sensors. This proposal aims to develop a unified computational framework which enables non-expert users to co-design and fabricate specialised physical activity sensors to address individualised sensing problems in applications such as rehabilitation, age-care and sports. Specifically, we will develop an analytical framework to classify complex sensing problems into fabricable primitive classes, namely i) conditional – limits of ac ....A Unified Framework to Rapidly Fabricate Individualised Activity Sensors. This proposal aims to develop a unified computational framework which enables non-expert users to co-design and fabricate specialised physical activity sensors to address individualised sensing problems in applications such as rehabilitation, age-care and sports. Specifically, we will develop an analytical framework to classify complex sensing problems into fabricable primitive classes, namely i) conditional – limits of activity, ii) differential – frequency of activity and iii) integrational – cumulative activity. And a co-design interface to synthesize them into complex activity sensors to fit individualised needs. Finally, we will evaluate the framework by deploying the created sensors in real-world settings and gathering data.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
The Intended and Unintended Impact of Policy for Adaptive Policy Management. The project aims to advance knowledge about the intended and unintended consequences of policy on health and well-being. It expects to innovate through new methods and novel data to integrate policy evaluation into the policy cycle in a timely fashion to prevent harm from occurring. It also leverages technology to track policy effects in real time. Expected outcomes of this project include new knowledge and enhanced pol ....The Intended and Unintended Impact of Policy for Adaptive Policy Management. The project aims to advance knowledge about the intended and unintended consequences of policy on health and well-being. It expects to innovate through new methods and novel data to integrate policy evaluation into the policy cycle in a timely fashion to prevent harm from occurring. It also leverages technology to track policy effects in real time. Expected outcomes of this project include new knowledge and enhanced policy infrastructure using new methods and interdisciplinary approaches. Significant benefits include improvements to: (1) policy management by government departments; (2) the health and wellbeing of the Australians they serve; (3) our Partners' capacity to consult governments on how technology can assist policy management. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100074
Funder
Australian Research Council
Funding Amount
$469,114.00
Summary
Future-proofing Australia’s care economy: A relational mobilities approach. This project aims to investigate the experiences of Australia’s migrant and mobile health workforce in the context of severe worker shortages worldwide. It will explore how healthcare workers’ family relationships and informal care responsibilities shape their migration decisions, experiences in the workplace and plans for the future. Expected outcomes include a comprehensive evidence-base about healthcare workers' exper ....Future-proofing Australia’s care economy: A relational mobilities approach. This project aims to investigate the experiences of Australia’s migrant and mobile health workforce in the context of severe worker shortages worldwide. It will explore how healthcare workers’ family relationships and informal care responsibilities shape their migration decisions, experiences in the workplace and plans for the future. Expected outcomes include a comprehensive evidence-base about healthcare workers' experiences of mobility, care, knowledge and skills to inform sustainable and person-centred policy solutions. The project should yield significant benefit by maximising Australia’s capacity to attract and retain a highly mobile workforce and their transnational knowledge and expertise to meet Australia’s growing care needs.Read moreRead less
Rebuilding Life After Migration for Young Refugees and Migrants . This project aims to provide a comprehensive understanding of refugee and migrant youth settlement experiences and its impact on psychological wellbeing and the role of support services. It will focus on the policies and practices that shape the settlement experiences of refugee and migrant youth which promote their psychological wellbeing. The study will provide settlement sectors and service providers with crucial new knowledge ....Rebuilding Life After Migration for Young Refugees and Migrants . This project aims to provide a comprehensive understanding of refugee and migrant youth settlement experiences and its impact on psychological wellbeing and the role of support services. It will focus on the policies and practices that shape the settlement experiences of refugee and migrant youth which promote their psychological wellbeing. The study will provide settlement sectors and service providers with crucial new knowledge of how settlement policies and practices can foster refugee and migrant psychological wellbeing. Outcomes of this project will include the development of research-based guides to good policy and practice in settlement services to improve psychological wellbeing outcomes for immigrant communities.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: 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
Transfer Learning for Genome Analysis and Personalised Recommendation. This project aims to improve the accuracy, adaptability, and comprehensiveness of health characteristic predictions and provide personalised recommendations for healthcare service and disease prevention. The deliverables include uncertainty learning and multi-source transfer learning methodologies for predictions based on genome analysis that distils and transfers useful knowledge from multiple sources into an Australian geno ....Transfer Learning for Genome Analysis and Personalised Recommendation. This project aims to improve the accuracy, adaptability, and comprehensiveness of health characteristic predictions and provide personalised recommendations for healthcare service and disease prevention. The deliverables include uncertainty learning and multi-source transfer learning methodologies for predictions based on genome analysis that distils and transfers useful knowledge from multiple sources into an Australian genome analysis model. A federated cross-domain recommender system will be developed to profile individuals and generate personalised recommendations. The outcomes are expected to create a paradigm shift in learning-based prediction and personalised recommendations to support healthcare services in complex environments. Read moreRead less
Sequential decision-making in dynamic and uncertain environments. Current machine learning and optimisation methods cannot well support sequential prediction and decision-making due to the dynamic nature and pervasive presence of big data. This project aims to create a foundation and technology for sequence and uncertainty learning, sequential and dynamic optimisation, and their integration. It is expected to improve robustness and mitigate the vulnerabilities of machine learning algorithms, to ....Sequential decision-making in dynamic and uncertain environments. Current machine learning and optimisation methods cannot well support sequential prediction and decision-making due to the dynamic nature and pervasive presence of big data. This project aims to create a foundation and technology for sequence and uncertainty learning, sequential and dynamic optimisation, and their integration. It is expected to improve robustness and mitigate the vulnerabilities of machine learning algorithms, to increase prediction accuracy and reliability in dynamic sequences, and to support decision-making in complex situations to achieve robust and adaptive results. Anticipated outcomes can help data scientists with state-of-the-art skills to manage sequential data and benefit data-enabled innovation in Australia.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220101075
Funder
Australian Research Council
Funding Amount
$415,820.00
Summary
Fuzzy transfer learning for real-time decision making under uncertainty. This project’s objective is to build new tools for the next generation of real-time decision making. As the datasphere grows more complex, meaningful decision support already requires a strong capacity for knowledge transfer, substantial robustness to uncertainty, and real-time analytics. Today’s methods are struggling to meet these challenges. The new schema to be devised combines fuzzy logic, transfer learning, reinforcem ....Fuzzy transfer learning for real-time decision making under uncertainty. This project’s objective is to build new tools for the next generation of real-time decision making. As the datasphere grows more complex, meaningful decision support already requires a strong capacity for knowledge transfer, substantial robustness to uncertainty, and real-time analytics. Today’s methods are struggling to meet these challenges. The new schema to be devised combines fuzzy logic, transfer learning, reinforcement learning and deep neural networks. These integrations will lay the foundations for real-time decision-making solutions over the next decade and will advance machine learning under uncertainty. Immediate applications include structural health monitoring, climate prediction and telecommunications maintenance. Read moreRead less