Linkage Infrastructure, Equipment And Facilities - Grant ID: LE240100131
Funder
Australian Research Council
Funding Amount
$539,000.00
Summary
Federated Omniverse Facilities for Smart Digital Futures. A world-first trans-disciplinary, -domain, and -institutional smart 3D omniverse R&D ecosystem AuVerse will be built in NSW, affiliated with Queensland, and accessible to academia and industry. AuVerse will support cloud-based, reality-virtuality-fused, immersive, interactive and secure future-oriented digital design, development, training and society. In the new era of digital innovation and paradigm shift, AuVerse will substantially boo ....Federated Omniverse Facilities for Smart Digital Futures. A world-first trans-disciplinary, -domain, and -institutional smart 3D omniverse R&D ecosystem AuVerse will be built in NSW, affiliated with Queensland, and accessible to academia and industry. AuVerse will support cloud-based, reality-virtuality-fused, immersive, interactive and secure future-oriented digital design, development, training and society. In the new era of digital innovation and paradigm shift, AuVerse will substantially boost Australia’s pivotal research leadership and business competitiveness in nurturing new-generation, collaborative and transformative digital R&D and talent pipeline. It will enable large-scale strategic business innovation and transformation including smart manufacturing and Industry 4.0.Read moreRead less
Towards Generalisable and Unbiased Dynamic Recommender Systems. This project aims to develop the foundations, including models, methodology, and algorithms for building generalisable and unbiased dynamic recommender systems to facilitate intelligent decision-making, prompt contextualised and personalised strategic plans, and support context-aware action recourse. To ensure that fundamental principles, such as fairness and transparency, are respected, a set of algorithms and techniques are propos ....Towards Generalisable and Unbiased Dynamic Recommender Systems. This project aims to develop the foundations, including models, methodology, and algorithms for building generalisable and unbiased dynamic recommender systems to facilitate intelligent decision-making, prompt contextualised and personalised strategic plans, and support context-aware action recourse. To ensure that fundamental principles, such as fairness and transparency, are respected, a set of algorithms and techniques are proposed to develop recommender systems in a more responsible manner. The result of this project will not only maintain Australia's leadership in this frontier research area, but also serve as an excellent vehicle for the education and training of Australia's next generation of scholars and engineers.Read moreRead less
Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, ....Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, statistics and different ways of aggregating information. By building a more complete theoretical map it is expected that new machine learning methods will be developed, but more importantly that machine learning will be able to be better integrated into larger socio-technical systems.Read moreRead less
Feature Learning for High-dimensional Functional Time Series. This project aims to develop new methods and theories for common features on high-dimensional functional time series observed in empirical applications. The significance includes addressing a key gap in adaptive and efficient feature learning, improving forecasting accuracy and understanding forecasting-driven factors comprehensively for empirical data. Expected outcomes involve advances in big data theory and easy-to-implement algori ....Feature Learning for High-dimensional Functional Time Series. This project aims to develop new methods and theories for common features on high-dimensional functional time series observed in empirical applications. The significance includes addressing a key gap in adaptive and efficient feature learning, improving forecasting accuracy and understanding forecasting-driven factors comprehensively for empirical data. Expected outcomes involve advances in big data theory and easy-to-implement algorithms for applied researchers. This project benefits not only advanced manufacturing by finding optimal stopping time for wood panel compression, but also superior forecasting for mortality in demography, climate data in environmental science, asset returns in finance, and electricity consumption in economics. Read moreRead less
Privacy-preserving data processing on the cloud. This project aims to address the current lack of privacy of user data processed by common cloud computing web servers, including email, business data, and confidential files. This project aims to develop new techniques in cryptography. The anticipated outcome is a suite of practical tools enabling common cloud computing processing operations such as search, statistical analysis, and multi-user access control, to be performed efficiently while pres ....Privacy-preserving data processing on the cloud. This project aims to address the current lack of privacy of user data processed by common cloud computing web servers, including email, business data, and confidential files. This project aims to develop new techniques in cryptography. The anticipated outcome is a suite of practical tools enabling common cloud computing processing operations such as search, statistical analysis, and multi-user access control, to be performed efficiently while preserving the data privacy. These tools should provide significant benefits to the privacy of cloud users, as well as financial and reputation benefits to the IT industry, by significantly reducing the likelihood of massive user data privacy breaches in the event of a cyber-hacking attack on the cloud server.Read moreRead less
Physico-chemical effects on long-time fluid transport for CO2 geostorage. This project aims to develop an efficient multi-scale laboratory-based modelling framework for the analysis of nonequilibrium transport and reaction processes occurring in CO2 storage scenarios. In a significant technological advance two non-destructive analysis techniques, Xray computed tomography and nuclear magnetic resonance, are combined with pore-scale simulations to address uncertainties in dynamic wettability alter ....Physico-chemical effects on long-time fluid transport for CO2 geostorage. This project aims to develop an efficient multi-scale laboratory-based modelling framework for the analysis of nonequilibrium transport and reaction processes occurring in CO2 storage scenarios. In a significant technological advance two non-destructive analysis techniques, Xray computed tomography and nuclear magnetic resonance, are combined with pore-scale simulations to address uncertainties in dynamic wettability alteration occurring during gravity driven convection. Expected outcomes are the in-situ characterisation of solid-surface interactions and predictions of multi-phase fluid flow. The project benefits the Australian resources sector by improving injectivity, storage efficiency and security of supercritical CO2 storage projects.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE170100033
Funder
Australian Research Council
Funding Amount
$900,000.00
Summary
Australian Acoustic Observatory: A network to monitor biodiversity. This project aims to create a terrestrial acoustic sensor network comprising 450 listening stations across Australia. Acoustic sensing transforms environmental science by recording vocal species 24/7, providing spatial and temporal data for ecosystem monitoring and research. Australia has leading research expertise in this emerging field, which is relevant to its fragile and mega-diverse environment. This project is expected to ....Australian Acoustic Observatory: A network to monitor biodiversity. This project aims to create a terrestrial acoustic sensor network comprising 450 listening stations across Australia. Acoustic sensing transforms environmental science by recording vocal species 24/7, providing spatial and temporal data for ecosystem monitoring and research. Australia has leading research expertise in this emerging field, which is relevant to its fragile and mega-diverse environment. This project is expected to enable and develop continental scale environmental monitoring, and the data generated will be made freely available to all online, enabling new science in understanding ecosystems, long-term environmental change, data visualisation and acoustic science.Read moreRead less
Socially Responsible Insurance in the Age of Artificial Intelligence. This project aims to discover the social costs and benefits of using Artificial Intelligence in insurance, and to design practical interventions—responsible design workshops, practical guidance, regulatory proposals, new algorithmic tools—that realise the benefits while mitigating the costs. It expects to generate new knowledge drawing on philosophy, law and sociology, working closely with practitioners at the forefront of dep ....Socially Responsible Insurance in the Age of Artificial Intelligence. This project aims to discover the social costs and benefits of using Artificial Intelligence in insurance, and to design practical interventions—responsible design workshops, practical guidance, regulatory proposals, new algorithmic tools—that realise the benefits while mitigating the costs. It expects to generate new knowledge drawing on philosophy, law and sociology, working closely with practitioners at the forefront of deploying AI in insurance. Expected outcomes include novel ethical AI-based approaches to product design, pricing and claims administration. This should benefit insurers and consumers, realising efficiency gains made possible by AI, without unacceptable costs to privacy, fairness, and the unaccountable exercise of power. Read moreRead less
Reliable and accurate statistical solutions for modern complex data. This project aims to develop novel methods for reliable and accurate statistical modelling with modern, complex correlated and error-prone data. The project expects to make significant strides towards future-proofing statistical data analysis, equipping practitioners with a suite of robust and computationally efficient methods which provide confidence in the stability and reproducibility of results obtained, while offering guar ....Reliable and accurate statistical solutions for modern complex data. This project aims to develop novel methods for reliable and accurate statistical modelling with modern, complex correlated and error-prone data. The project expects to make significant strides towards future-proofing statistical data analysis, equipping practitioners with a suite of robust and computationally efficient methods which provide confidence in the stability and reproducibility of results obtained, while offering guarantees on their transferability over a range of populations. This will provide important benefits as they are applied in predicting endangered marine species for fisheries conservation, and in enhancing our national understanding of the relationship between education achievement and financial success. Read moreRead less
Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an ....Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an open-source tool that can capture precision correlations between deep code features and diverse vulnerabilities to pinpoint emerging vulnerabilities without the need for bug specifications. Significant benefits include greatly improved quality, reliability and security for modern software systems.Read moreRead less