Ultrahigh-speed optical transport for sustaining the internet growth. Our society has entered an information era centred around the Internet. This project aims to study novel transport technologies to construct optical backbone networks supporting the Internet traffic. The project will keep Australia at the leading edge of exciting Terabit technologies as well as create commercial opportunities in Australia.
Development of methods and algorithms to support multidisciplinary optimisation. This project will aim to develop a number of novel and computationally efficient schemes to deal with the key challenges facing multidisciplinary optimisation. These advancements will allow us to solve a number of challenging and intractable problems in science and engineering.
Optimisation for next generation machine learning. As more and more data are being collected, it is important to build intelligent systems which will can analyse these data efficiently. This project will take design and analyse new algorithms which take advantage of emerging paradigms in hardware such as multicore processors, graphic processing units (GPU), and cluster computers to achieve this goal.
Regularisation methods of inverse problems: theory and computation. This project aims to investigate regularisation methods for inverse problems which are ill-posed in the sense that their solutions depend discontinuously on the data. When only noisy data is available, regularisation methods define stable approximate solutions by replacing the original inverse problem with a family of well-posed neighbouring problems monitored by a so-called regularisation parameter. The project expects to devel ....Regularisation methods of inverse problems: theory and computation. This project aims to investigate regularisation methods for inverse problems which are ill-posed in the sense that their solutions depend discontinuously on the data. When only noisy data is available, regularisation methods define stable approximate solutions by replacing the original inverse problem with a family of well-posed neighbouring problems monitored by a so-called regularisation parameter. The project expects to develop purely data-driven rules to choose the regularisation parameter and show how they work in theory, and in practice. It will also develop convex framework, acceleration strategies as well as preconditioning and splitting ideas to design efficient regularisation solvers.Read moreRead less
A New Optimization Approach for Tensor Extreme Eigenvalue Problems: Modern Techniques
for Multi-relational Data Analysis. Nowadays, we often encounter complex multi-relational data whose objects have interactions among themselves based on different relations. These multi-relational data can be mathematically modelled as tensors. The tensor extreme eigenvalue problem, which is concerned with extracting the most significant qualitative information from multi-relational data, plays a key role in m ....A New Optimization Approach for Tensor Extreme Eigenvalue Problems: Modern Techniques
for Multi-relational Data Analysis. Nowadays, we often encounter complex multi-relational data whose objects have interactions among themselves based on different relations. These multi-relational data can be mathematically modelled as tensors. The tensor extreme eigenvalue problem, which is concerned with extracting the most significant qualitative information from multi-relational data, plays a key role in modern data analysis. This project aims at developing innovative global optimisation frameworks and reliable numerical methods for tensor extreme eigenvalue problems, and applying the proposed methods to solve various practical problems arising from important application areas such as modern data analysis, medical imaging science and signal processing.Read moreRead less
Optimal maintenance planning for critical mining and energy infrastructure. This project aims to develop cutting-edge mathematical algorithms for optimising maintenance activities in the mining and energy sectors. Such maintenance activities are prone to budget and time overruns due to poor planning - the result of outdated, inefficient manual processes. The project is expected to result in new maintenance planning methods, underpinned by rigorous mathematical theory, for reducing manual interve ....Optimal maintenance planning for critical mining and energy infrastructure. This project aims to develop cutting-edge mathematical algorithms for optimising maintenance activities in the mining and energy sectors. Such maintenance activities are prone to budget and time overruns due to poor planning - the result of outdated, inefficient manual processes. The project is expected to result in new maintenance planning methods, underpinned by rigorous mathematical theory, for reducing manual intervention and optimising both short- and long-term maintenance based on real-time sensor data. These new methods will be powerful tools for tackling the complexity of large-scale, time-critical maintenance projects, driving productivity in the resources industry and fostering collaboration between mathematicians and engineers.Read moreRead less
Deep Interaction Learning in Unlabelled Big Data and Complex Systems. This project aims to effectively model intricate interactions deeply embedded in unlabelled big data and complex systems, which are often hierarchical, heterogeneous, contextual, dynamic or even contrastive. Learning such interactions is the keystone of robust data science and for realizing the value of big data but it poses significant challenges and knowledge gaps to existing data analytics and learning systems. The expected ....Deep Interaction Learning in Unlabelled Big Data and Complex Systems. This project aims to effectively model intricate interactions deeply embedded in unlabelled big data and complex systems, which are often hierarchical, heterogeneous, contextual, dynamic or even contrastive. Learning such interactions is the keystone of robust data science and for realizing the value of big data but it poses significant challenges and knowledge gaps to existing data analytics and learning systems. The expected outcomes include new-generation theories and methods for the unsupervised learning of complex interactions in real-life big data, which are anticipated to enable the intrinsic processing of big data complexities and substantially enhance Australia’s leadership in frontier data science research and applications. Read moreRead less
Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in bi ....Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in biomedical image analysis. This project will lead to fundamental contributions as well as techniques that address both problems: extraction of relevant features information from multisubject brain imaging data sets of the same modality or from fusion of brain imaging data sets collected from multimodalities.Read moreRead less
Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-s ....Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-supervised learning that are robust to failures and provide explainable decisions for object detection and scene segmentation. The outcomes are expected to advance theory in robust deep learning and benefit 3D mapping, surveying, infrastructure monitoring, transport and robotics industries.Read moreRead less
Engineering Artificial Intelligence: A Spatial Representation and Reasoning Perspective. Spatial information is important in areas of national interest such as mining and exploration, environmental monitoring and planning, emergency response, and defence. Mission control centres, for instance, receive different forms of spatial data from satellites, radar, or people on the ground. They have to process the input data and make intelligent decisions in a very limited time. Intelligent systems that ....Engineering Artificial Intelligence: A Spatial Representation and Reasoning Perspective. Spatial information is important in areas of national interest such as mining and exploration, environmental monitoring and planning, emergency response, and defence. Mission control centres, for instance, receive different forms of spatial data from satellites, radar, or people on the ground. They have to process the input data and make intelligent decisions in a very limited time. Intelligent systems that are able to assist with processing different forms of spatial data efficiently and that offer reliable decision support are essential for improving the quality and reliability of such applications. This research enables future intelligent systems with these capabilities. This will directly benefit applications in areas of national interest.Read moreRead less