Modelling Adversarial Noise for Trustworthy Data Analytics. Adversarial robustness is a core property of trustworthy machine learning. This project aims to equip machines with the ability to model adversarial noise for defending adversarial attacks. The project expects to produce the next great step for artificial intelligence – the potential to robustly explore and exploit deceptive data. Expected outcomes of this project include theoretical foundations for modelling adversarial noise and the n ....Modelling Adversarial Noise for Trustworthy Data Analytics. Adversarial robustness is a core property of trustworthy machine learning. This project aims to equip machines with the ability to model adversarial noise for defending adversarial attacks. The project expects to produce the next great step for artificial intelligence – the potential to robustly explore and exploit deceptive data. Expected outcomes of this project include theoretical foundations for modelling adversarial noise and the next generation of intelligent systems to accommodate data in a noisy and hostile environment. This should benefit science, society, and the economy nationally and internationally through the applications to trustworthily analyse their corresponding complex data. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240101089
Funder
Australian Research Council
Funding Amount
$436,847.00
Summary
Trustworthy Hypothesis Transfer Learning. It is urgent to develop a new hypothesis transfer learning scheme that can overcome potential risks when finetuning unreliable large-scale pre-trained models. This project aims to develop an advanced and reliable scheme of hypothesis transfer learning, called Trustworthy Hypothesis Transfer Learning (TrustHTL). A new theoretically guaranteed heterogeneous hypothesis transfer learning framework will be developed to handle heterogeneous situations; a metho ....Trustworthy Hypothesis Transfer Learning. It is urgent to develop a new hypothesis transfer learning scheme that can overcome potential risks when finetuning unreliable large-scale pre-trained models. This project aims to develop an advanced and reliable scheme of hypothesis transfer learning, called Trustworthy Hypothesis Transfer Learning (TrustHTL). A new theoretically guaranteed heterogeneous hypothesis transfer learning framework will be developed to handle heterogeneous situations; a methodology to disinherit risks of pre-trained models and a new fuzzy relation based distributional discrepancy in heterogeneous transfer learning scenarios. The outcomes should significantly improve the reliability of machine learning with benefits for safety learning in data analytics.Read moreRead less
Self-supervised feature learning for rapid processing of marine imagery. Fast and reliable quantitative estimates of marine environmental health are needed for scientific studies, design and management of protected areas, and regulatory compliance of industrial activity in the ocean. Australia is collecting seafloor images at increasing rates but expert annotations are not keeping up, meaning that typical machine learning approaches struggle. This project will develop self-supervised techniques ....Self-supervised feature learning for rapid processing of marine imagery. Fast and reliable quantitative estimates of marine environmental health are needed for scientific studies, design and management of protected areas, and regulatory compliance of industrial activity in the ocean. Australia is collecting seafloor images at increasing rates but expert annotations are not keeping up, meaning that typical machine learning approaches struggle. This project will develop self-supervised techniques that use large amounts of unlabeled data to enhance performance. Our design takes advantage of additional information available for marine imagery such as geolocation and remote sensing context. We will explore how these representations can guide additional sampling and improve performance in classification tasks.Read moreRead less
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.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240101035
Funder
Australian Research Council
Funding Amount
$450,760.00
Summary
Charting the brain's wiring over the human lifespan. This project aims to produce a large-scale model of brain wiring over the human lifespan by utilising normative modelling approaches on state-of-the-art diffusion magnetic resonance imaging (diffusion MRI) data. This project expects to generate new understanding of how the brain's connections change with age in healthy individuals. Expected outcomes of this project include a reference chart for healthy brain wiring, and major advances in diffu ....Charting the brain's wiring over the human lifespan. This project aims to produce a large-scale model of brain wiring over the human lifespan by utilising normative modelling approaches on state-of-the-art diffusion magnetic resonance imaging (diffusion MRI) data. This project expects to generate new understanding of how the brain's connections change with age in healthy individuals. Expected outcomes of this project include a reference chart for healthy brain wiring, and major advances in diffusion MRI data harmonisation approaches. This should provide significant benefits for the translation of advanced diffusion MRI methods, as normative charts for brain wiring will be made broadly available. This could have broad implications for interpreting individual diffusion MRI scans in future.Read moreRead less
Causal Knowledge-Empowered Adaptive Federated Learning. Federated learning tools are a promising framework for collaborative machine learning (ML) that also maintain data privacy; however, their ability to model heterogeneous data remains a key challenge. This project aims to develop a new learning scheme for coordinated training of ML models that successfully bridges variable data distributions. The framework proposed will be the first globally that can use causal knowledge to 1) handle data he ....Causal Knowledge-Empowered Adaptive Federated Learning. Federated learning tools are a promising framework for collaborative machine learning (ML) that also maintain data privacy; however, their ability to model heterogeneous data remains a key challenge. This project aims to develop a new learning scheme for coordinated training of ML models that successfully bridges variable data distributions. The framework proposed will be the first globally that can use causal knowledge to 1) handle data heterogeneity across devices and 2) address the real-world challenges when only a subset of devices have labelled data. Expected outcomes and benefits include the theoretical underpinnings and algorithms of causality-based collaborative training of ML models while better preserving the users’ data privacy.Read moreRead less
Mechanisms for Improved Ductility of Magnesium Alloys. The work will lead to more ductile magnesium alloys. These alloys will be more readily formed into automotive components. The lighter cars that will result will be cheaper to run and more environmentally friendly. The exchange of key researchers that will occur under this proposal will provide an exciting injection of expertise into the partner organisations from which students will greatly benefit. The work will also open up access to state ....Mechanisms for Improved Ductility of Magnesium Alloys. The work will lead to more ductile magnesium alloys. These alloys will be more readily formed into automotive components. The lighter cars that will result will be cheaper to run and more environmentally friendly. The exchange of key researchers that will occur under this proposal will provide an exciting injection of expertise into the partner organisations from which students will greatly benefit. The work will also open up access to state-of-the-art equipment in the collaborating laboratories.Read moreRead less
On demand three-dimensional printing of stainless steel parts. On demand three-dimensional printing of stainless steel parts. This project aims to revolutionize the security of supply of critical stainless steel parts by producing them on-site and on demand, using three dimensional metal printing. Australia’s oil and gas industry uses tonnes of stainless steel for critical processing components in production plants. Australia is also one of the few developed nations without appreciable productio ....On demand three-dimensional printing of stainless steel parts. On demand three-dimensional printing of stainless steel parts. This project aims to revolutionize the security of supply of critical stainless steel parts by producing them on-site and on demand, using three dimensional metal printing. Australia’s oil and gas industry uses tonnes of stainless steel for critical processing components in production plants. Australia is also one of the few developed nations without appreciable production and processing facilities for stainless steels, so relies on specialist overseas suppliers. This is a major risk to the industry, which stores billions of dollars’ worth of replacement parts, including stainless steels, in inventory. This project should reduce reliance on overseas steel suppliers and free up hundreds of millions of dollars of capital invested in the inventory stores of replacement stainless steel parts.Read moreRead less
Sustainable and robust Australian Ni-based superalloy manufacturing. This project aims to solve challenges related to microstructural defect formation in the manufacturing of a critical Ni-based superalloy. It will generate new knowledge on its microstructure evolution and defect origin via a combined experimental and computational approach. Expected outcomes are advanced manufacturing routes with higher yield of defect free materials, using more scrap as input. This will enable robust and susta ....Sustainable and robust Australian Ni-based superalloy manufacturing. This project aims to solve challenges related to microstructural defect formation in the manufacturing of a critical Ni-based superalloy. It will generate new knowledge on its microstructure evolution and defect origin via a combined experimental and computational approach. Expected outcomes are advanced manufacturing routes with higher yield of defect free materials, using more scrap as input. This will enable robust and sustainable alloy manufacturing for power generation, defence, and aerospace industries. Commercial benefits are opportunities to domestically source alloys with reduced dependency on international trade. Environmental and societal benefits include lower emissions due to better mechanical design and workforce training.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE140101759
Funder
Australian Research Council
Funding Amount
$385,720.00
Summary
A novel fundamental approach to enable net shape manufacturing of low-cost high-performance titanium alloys . Oxygen is the bottleneck issue of titanium powder metallurgy, which radically deteriorates the ductility of titanium. This project aims to develop the essential fundamental knowledge and technical solutions to mitigate the detrimental effect of oxygen on the ductility of as-sintered titanium products and enable the net-shape fabrication of low-cost high-performance titanium alloys. This ....A novel fundamental approach to enable net shape manufacturing of low-cost high-performance titanium alloys . Oxygen is the bottleneck issue of titanium powder metallurgy, which radically deteriorates the ductility of titanium. This project aims to develop the essential fundamental knowledge and technical solutions to mitigate the detrimental effect of oxygen on the ductility of as-sintered titanium products and enable the net-shape fabrication of low-cost high-performance titanium alloys. This will be achieved by utilising the inexpensive and unique titanium hydride powder, rather than titanium metal powder, and by developing effective oxygen scavengers. The outcomes will form a robust basis for the creation of a viable titanium hydride powder metallurgy business.Read moreRead less