Brain-skull interface: discovering the missing piece of head biomechanics. Overall objective of this project is to measure, mathematically describe and implement in software mechanical properties of brain-skull interface – a critical component of current large and sophisticated computational models of the brain and the last missing piece of brain biomechanics knowledge. This will allow increased reliability of comprehensive biomechanical models used to simulate realistic injury and surgery scena ....Brain-skull interface: discovering the missing piece of head biomechanics. Overall objective of this project is to measure, mathematically describe and implement in software mechanical properties of brain-skull interface – a critical component of current large and sophisticated computational models of the brain and the last missing piece of brain biomechanics knowledge. This will allow increased reliability of comprehensive biomechanical models used to simulate realistic injury and surgery scenarios.
The problem is significant and urgent. Every year in Australia, there are over 22,000 cases of traumatic brain injury, some of which could be prevented by better passive and active countermeasures; and over 12,000 neurosurgical procedures that surgical simulation could make more accurate and therefore safer.Read moreRead less
3D Diffusion Models for Generating and Understanding 3D Scenes. Diffusion models, such as DALL-E2 and Imagen, have achieved remarkable success in generating photorealistic images and hold promise to solve long-standing computer vision problems. However, 3D scene generation remains unexplored. This research project aims to bridge the gap by developing 3D diffusion models capable of generating complete 3D scenes. This will advance our theoretical understanding of diffusion in complex 3D environmen ....3D Diffusion Models for Generating and Understanding 3D Scenes. Diffusion models, such as DALL-E2 and Imagen, have achieved remarkable success in generating photorealistic images and hold promise to solve long-standing computer vision problems. However, 3D scene generation remains unexplored. This research project aims to bridge the gap by developing 3D diffusion models capable of generating complete 3D scenes. This will advance our theoretical understanding of diffusion in complex 3D environments and open up new possibilities for applications in fields such as virtual reality, architecture, and city planning. The proposed 3D diffusion models will also enhance the accuracy of computer vision tasks related to 3D scene understanding, such as object detection, tracking, and semantic segmentation.Read moreRead less
Intelligent Virtual Human Companions. This research aims to develop intelligent virtual human companions that can seemingly integrate our immediate physical environment and understand their surroundings including people’s emotions, behaviours, actions and interactions. Such a technology will be enabled by leveraging recent advances in mixed/augmented reality technologies, and by developing innovative artificial intelligence and computer vision and graphics algorithms for dynamic real-world envir ....Intelligent Virtual Human Companions. This research aims to develop intelligent virtual human companions that can seemingly integrate our immediate physical environment and understand their surroundings including people’s emotions, behaviours, actions and interactions. Such a technology will be enabled by leveraging recent advances in mixed/augmented reality technologies, and by developing innovative artificial intelligence and computer vision and graphics algorithms for dynamic real-world environments. Unlike robots, the proposed technology will be low cost, readily deployable and customisable, and will not have any physical limitations or maintenance requirements. It will thus have a wide range of applications from elderly care, healthcare care to educational training.Read moreRead less
Shape4D: Modelling the Spatiotemporal Deformation Patterns in 3D Shapes. This research will develop new mathematical methods and algorithms that will enable the use of population-level longitudinal studies to model the spatial and temporal deformation patterns in 3D biological objects. Using novel geometric and deep learning techniques, it will create new methods that will allow the characterization of how the 3D shape of objects deforms with ageing, disease progression and interaction with thei ....Shape4D: Modelling the Spatiotemporal Deformation Patterns in 3D Shapes. This research will develop new mathematical methods and algorithms that will enable the use of population-level longitudinal studies to model the spatial and temporal deformation patterns in 3D biological objects. Using novel geometric and deep learning techniques, it will create new methods that will allow the characterization of how the 3D shape of objects deforms with ageing, disease progression and interaction with their environment, and the simulation of spatiotemporal deformations in anatomical organs. Benefits include a better understanding of growth processes, predictive models of how degenerative diseases progress and a computational framework that will assist in designing proper mitigation and intervention strategies.Read moreRead less
Accuracy and cost-effectiveness of technology-assisted dietary assessment. This project aims to compare leading methods for technology-assisted dietary assessment. Excessive cost and questionable accuracy limit the routine use of dietary assessment and undermine decision making in Australia. This project intends to compare three technology methods of assessing diet with the current standard recall method used in population surveys in order to confirm if the use of food images and automated metho ....Accuracy and cost-effectiveness of technology-assisted dietary assessment. This project aims to compare leading methods for technology-assisted dietary assessment. Excessive cost and questionable accuracy limit the routine use of dietary assessment and undermine decision making in Australia. This project intends to compare three technology methods of assessing diet with the current standard recall method used in population surveys in order to confirm if the use of food images and automated methods provide new approaches to improve accuracy and consumer acceptability. Expected outcomes of this project include more accurate and acceptable methods of assessing dietary intake. These findings will inform decision making for researchers, policy makers and practitioners in Australia, and potentially lead to more regular population surveillance.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
Tensor and Hypergraph Methods in Fitting Visual Data. This proposal will put an important class of clustering (extracting data that should fit a geometric model) on a more solid theoretical foundation. This will lead to better understanding of how to certify outcomes, efficiency, reliability etc. The type of clustering under consideration is relevant to many problems in machine learning and computer vision, as well as data mining and a wide variety of other settings.
Next generation gas separations via innovative adsorption technologies. This project aims to develop new gas separation technologies that combine novel materials and pressure swing adsorption cycles to deliver inexpensive industrial processes capable of both high recovery and high purity products. The project will advance our ability to manipulate the phenomenon of regulated guest admission into microporous materials, and integrate such materials within new types of dual-reflux adsorption cycles ....Next generation gas separations via innovative adsorption technologies. This project aims to develop new gas separation technologies that combine novel materials and pressure swing adsorption cycles to deliver inexpensive industrial processes capable of both high recovery and high purity products. The project will advance our ability to manipulate the phenomenon of regulated guest admission into microporous materials, and integrate such materials within new types of dual-reflux adsorption cycles that deliver multiple refined gas products. Successful implementation of these industrial developments will increase Australia's access to cheap supplies of natural gas, encourage the broader use of biomass, lower the carbon emissions of industrial processes, and efficiently recover high-value compounds only present at trace concentrations.Read moreRead less
Defense against adversarial attacks on deep learning in computer vision. Computer vision applications rely heavily on deep learning, which is highly vulnerable to being fooled by adding subtle perturbations to object/image textures that are imperceptible to humans. This project aims to develop defense mechanisms to detect and remove adversarial patterns from the input images. The project expects to advance knowledge in understanding the vulnerabilities of deep learning, and to design deep learni ....Defense against adversarial attacks on deep learning in computer vision. Computer vision applications rely heavily on deep learning, which is highly vulnerable to being fooled by adding subtle perturbations to object/image textures that are imperceptible to humans. This project aims to develop defense mechanisms to detect and remove adversarial patterns from the input images. The project expects to advance knowledge in understanding the vulnerabilities of deep learning, and to design deep learning architectures that are inherently robust. The outcomes of this project will increase the security and reliability of computer vision by detecting, reporting and nullifying such attacks and will benefit the general public and industry on many fronts.Read moreRead less
Energy big data analytics from a cybersecurity perspective. This project aims to develop a framework on energy big data analytics from security and privacy perspectives. Unlike other big data analytics such as social network big data analytics, energy big data analytics involve research challenges on how to cope with real-time tight cyber-physical couplings, and security/safety of the smart grid system. This project will develop advanced data-driven algorithms that are capable of detecting coord ....Energy big data analytics from a cybersecurity perspective. This project aims to develop a framework on energy big data analytics from security and privacy perspectives. Unlike other big data analytics such as social network big data analytics, energy big data analytics involve research challenges on how to cope with real-time tight cyber-physical couplings, and security/safety of the smart grid system. This project will develop advanced data-driven algorithms that are capable of detecting coordinated cyber-attacks that will potentially lead to catastrophic cascaded failures; and develop new solutions in detecting the false data-injection attacks that are conventionally considered as unobservable. This project will provide the benefit of enhancing our national critical infrastructure's security.Read moreRead less