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Field of Research : Artificial intelligence
Australian State/Territory : VIC
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  • Active Funded Activity

    Discovery Projects - Grant ID: DP240100963

    Funder
    Australian Research Council
    Funding Amount
    $510,261.00
    Summary
    Congestion control in complex networks with higher-order interactions. Traffic congestion significantly costs the Australian economy and environment. This project aims to develop ground-breaking network models of urban traffic systems to build a new congestion control framework. The purpose of network modelling is to capture the interdependence between different parts of traffic systems, which facilitates studying congestion cascade within the network. The project expects to generate next genera .... Congestion control in complex networks with higher-order interactions. Traffic congestion significantly costs the Australian economy and environment. This project aims to develop ground-breaking network models of urban traffic systems to build a new congestion control framework. The purpose of network modelling is to capture the interdependence between different parts of traffic systems, which facilitates studying congestion cascade within the network. The project expects to generate next generation of network models for more effective congestion control. Expected outcomes include novel congestion control technologies that adjust traffic signals in real-time to optimally utilise the available road space. This should provide significant economic and environmental benefits to Australians by easing traffic jams.
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    Active Funded Activity

    Linkage Projects - Grant ID: LP230100379

    Funder
    Australian Research Council
    Funding Amount
    $349,108.00
    Summary
    An explainability oriented approach to manage dependent supply chain risks. This project aims to help supply chain companies model the impact on their operations by capturing the uncertainties impacting their upstream suppliers. In the current uncertain business environment, the project's outcome will benefit service-based industries to have an enhanced understanding of their operating environment and take decisions accordingly to avoid failures. This will significantly increase the productivity .... An explainability oriented approach to manage dependent supply chain risks. This project aims to help supply chain companies model the impact on their operations by capturing the uncertainties impacting their upstream suppliers. In the current uncertain business environment, the project's outcome will benefit service-based industries to have an enhanced understanding of their operating environment and take decisions accordingly to avoid failures. This will significantly increase the productivity of Australian service-based industries across different domains. The expected outcome is that it generates new knowledge by which risk managers of a focal company can conjointly consider risk identification/assessment with risk management analysis to develop explainable strategies for managing uncertainties.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP230101540

    Funder
    Australian Research Council
    Funding Amount
    $480,000.00
    Summary
    Advanced Machine Learning with Bilevel Optimization. There is an urgent need to develop a new machine learning (ML) paradigm that can overcome data-privacy and model-size constraints in real-world applications. This project aims to develop an advanced paradigm of ML with bilevel optimisation, called bilevel ML. A theoretically-guaranteed fast approximate solver and a new fuzzy bilevel learning framework will be developed to achieve the aim in complex situations; a methodology to transfer knowled .... Advanced Machine Learning with Bilevel Optimization. There is an urgent need to develop a new machine learning (ML) paradigm that can overcome data-privacy and model-size constraints in real-world applications. This project aims to develop an advanced paradigm of ML with bilevel optimisation, called bilevel ML. A theoretically-guaranteed fast approximate solver and a new fuzzy bilevel learning framework will be developed to achieve the aim in complex situations; a methodology to transfer knowledge and an approach to fast-adapt bilevel optimization solutions when required computing resources change. The anticipated outcomes should significantly improve the reliability of ML with benefits for safety learning and computing resource optimisation in ML-based data analytics.
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