A cloud computing environment for managing foreign exchange risk. This project involves using parallel computing and machine learning to improve the real-time handling of a bank's foreign exchange risk. It will lead to improved techniques to manage exchange rate variations, enabling banks to better assess their risk and will ultimately lead to improved services for Australian companies.
Switching Dynamics Approach for Distributed Global Optimisation . This project aims to create a breakthrough switching dynamics approach and new technology to speed up finding optimal solutions. It will develop a distributed switching dynamics based optimisation scheme for global optimisation problems in industrial big-data environments where timely decision making is required. It will result in a practical technology for industry optimisation problems such as economic energy dispatch in smart g ....Switching Dynamics Approach for Distributed Global Optimisation . This project aims to create a breakthrough switching dynamics approach and new technology to speed up finding optimal solutions. It will develop a distributed switching dynamics based optimisation scheme for global optimisation problems in industrial big-data environments where timely decision making is required. It will result in a practical technology for industry optimisation problems such as economic energy dispatch in smart grids and optimal charging and discharging tasks in a large network of electric vehicles, helping Australian power industry improve efficiency and security, as well as training the next generation scientists and engineers for Australia in this emerging field.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL200100204
Funder
Australian Research Council
Funding Amount
$3,137,608.00
Summary
Trustworthy Artificial Intelligence. This project aims to understand how to build AI systems that humans can trust. It does so by studying how to make such systems fair, explainable, auditable, preserving of privacy and verifiable. Outputs will include tools to build trustworthy AI systems, as well as policy recommendations to complement the technical tools. This should provide significant economic and societal benefits as decisions in both the public and private sector are increasingly being ha ....Trustworthy Artificial Intelligence. This project aims to understand how to build AI systems that humans can trust. It does so by studying how to make such systems fair, explainable, auditable, preserving of privacy and verifiable. Outputs will include tools to build trustworthy AI systems, as well as policy recommendations to complement the technical tools. This should provide significant economic and societal benefits as decisions in both the public and private sector are increasingly being handed over to computers.Read moreRead less
Modelling the Development and Evolution of Business Relations and Networks as Complex Adaptive Systems using Agent Based Models. This research develops models that will allow managers and policy makers to play more effective roles in the development and evolution of collaborative business relations and networks that lead to greater efficiency, industry innovation, and firm competitiveness, which is a key focus of corporate management and government trade and industry policy. It will also enhance ....Modelling the Development and Evolution of Business Relations and Networks as Complex Adaptive Systems using Agent Based Models. This research develops models that will allow managers and policy makers to play more effective roles in the development and evolution of collaborative business relations and networks that lead to greater efficiency, industry innovation, and firm competitiveness, which is a key focus of corporate management and government trade and industry policy. It will also enhance Australia's resources and expertise in understanding and modelling complex adaptive systems, help develop education resources and training programs for practitioners and researchers in this fast growing area of theory and research, strengthen links with leading researchers and centres, and produce a doctorate in the area.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130101605
Funder
Australian Research Council
Funding Amount
$289,000.00
Summary
Composing machine learning via market mechanisms. This project aims to better understand connections between learning algorithms and markets as aggregators of information and develop new, principled techniques for combining predictions. This will improve our ability to construct systems that make predictions based on multiple, complex and structured sources of data.
Universal game-playing systems for randomised and imperfect-information games. This project will develop an artificial intelligence system that you can tell the rules of any new game and that then all by itself learns to play that game. The innovative aspect is that our system will be able to handle games with elements of chance, like dice, and where some information is hidden, as for example in most card games.
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