Discovery Early Career Researcher Award - Grant ID: DE140100099
Funder
Australian Research Council
Funding Amount
$389,575.00
Summary
The importance of glia in the development of healthy blood vessels in the eye. The formation of healthy blood vessels in the eye is critical for vision. An important peptide in blood vessel formation throughout the body, including the eye, is Angiotensin II. Specific retinal cells named astrocytes and microglia are suggested to be involved in vessel formation. However, the way these cells control this formation is unknown. Using innovative techniques, this project will investigate how retinal as ....The importance of glia in the development of healthy blood vessels in the eye. The formation of healthy blood vessels in the eye is critical for vision. An important peptide in blood vessel formation throughout the body, including the eye, is Angiotensin II. Specific retinal cells named astrocytes and microglia are suggested to be involved in vessel formation. However, the way these cells control this formation is unknown. Using innovative techniques, this project will investigate how retinal astrocytes and microglia control glial-vascular communication and blood vessel formation via Angiotensin II. The information gained from this proposal is critical to our understanding of both normal retinal development and blood vessel maintenance in both infants and those of older ages.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120101931
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Functional imaging with cellular resolution in the living eye. Adaptive optics allows the visualisation of individual cells and capillaries in the living human eye. This project will use adaptive optics to explore the normal function of these microscopic objects, and how this function changes in eyes suffering from disease. This will aid in developing new ways to diagnose and treat debilitating eye diseases.
Discovery Early Career Researcher Award - Grant ID: DE140100320
Funder
Australian Research Council
Funding Amount
$394,371.00
Summary
Understanding cone photoreceptor migration and cell death mechanisms . Normal vision requires functional and correctly located cone photoreceptor cells. Many genetic mutations, however, impair the correct migration of these cells during development and ultimately cause cell death. This project will investigate, for the first time, the casual link between the migration of cone cells and activation of cell death mechanisms. A coordinated approach, using a range of molecular techniques, will be use ....Understanding cone photoreceptor migration and cell death mechanisms . Normal vision requires functional and correctly located cone photoreceptor cells. Many genetic mutations, however, impair the correct migration of these cells during development and ultimately cause cell death. This project will investigate, for the first time, the casual link between the migration of cone cells and activation of cell death mechanisms. A coordinated approach, using a range of molecular techniques, will be used to determine which factors are essential for normal development, correct spatial location and survival of cone photoreceptors within the mammalian retina. This will provide a major step forward in our knowledge of the processes involved in the spatial deployment of cones and the developmental organisation of the retina.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230100477
Funder
Australian Research Council
Funding Amount
$421,554.00
Summary
Advancing Human Perception: Countering Evolving Malicious Fake Visual Data. The aim of this project is to provide new effective and generalisable deepfake detection methods for automatically detecting maliciously manipulated visual data generated by misused artificial intelligence (AI) techniques. It will present innovative computer vision and image processing knowledge and techniques, enabling the developed methods to advance human perception in recognising fake data, enhance cybersecurity, and ....Advancing Human Perception: Countering Evolving Malicious Fake Visual Data. The aim of this project is to provide new effective and generalisable deepfake detection methods for automatically detecting maliciously manipulated visual data generated by misused artificial intelligence (AI) techniques. It will present innovative computer vision and image processing knowledge and techniques, enabling the developed methods to advance human perception in recognising fake data, enhance cybersecurity, and protect privacy in AI applications. The anticipated outcomes should provide significant benefits to a wide range of applications, such as providing timely alerts to the media, government organisations, and the industry about misleading fake visual data, and preventing financial crimes on synthetic identity fraud.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130100868
Funder
Australian Research Council
Funding Amount
$336,284.00
Summary
Neural mechanisms of inhibitory control of human speech in stutterers and non-stutterers. Stopping oneself from speaking is a crucial communication function. In people who stutter, a disorder of this function causes their debilitating speech problem. This project will use cutting edge neuroimaging techniques to reveal how the brain stops speech in stutterers and in fluent speakers.
Discovery Early Career Researcher Award - Grant ID: DE240100967
Funder
Australian Research Council
Funding Amount
$366,000.00
Summary
Open-world computer vision by detecting and tracking hierarchical objects. This project examines the problem of detecting and tracking objects using computer vision. A fundamental limitation of current algorithms is that they require labelled training data for every object class and therefore cannot be trusted to operate in unconstrained environments. This project aims to address this limitation using novel techniques that incorporate hierarchical relationships between object classes. Expected o ....Open-world computer vision by detecting and tracking hierarchical objects. This project examines the problem of detecting and tracking objects using computer vision. A fundamental limitation of current algorithms is that they require labelled training data for every object class and therefore cannot be trusted to operate in unconstrained environments. This project aims to address this limitation using novel techniques that incorporate hierarchical relationships between object classes. Expected outcomes include new paradigms for algorithm design and evaluation, and establishing the problem as a focus of international research. The key practical benefit would be to accelerate the wider deployment of visual perception in applications such as autonomous vehicles, interactive robotics, and video analysis.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230101058
Funder
Australian Research Council
Funding Amount
$437,254.00
Summary
Glass-box Deep Machine Perception for Trustworthy Artificial Intelligence. Explainability and Transparency are the key values for development and deployment of Artificial Intelligence (AI) in Australia’s AI Ethics Framework for industry and governments. This project aims to build new tools to make the central technology of AI - deep learning - transparent and explainable. Its expected outputs are novel theory-driven algorithms and unconventional foundational blocks for deep learning that will al ....Glass-box Deep Machine Perception for Trustworthy Artificial Intelligence. Explainability and Transparency are the key values for development and deployment of Artificial Intelligence (AI) in Australia’s AI Ethics Framework for industry and governments. This project aims to build new tools to make the central technology of AI - deep learning - transparent and explainable. Its expected outputs are novel theory-driven algorithms and unconventional foundational blocks for deep learning that will allow humans to clearly interpret the reasoning process of this technology, which is currently not possible. It is expected to significantly advance our knowledge in machine intelligence and perception. Due to their fundamental nature, the project outcomes are likely to benefit industry and scientific frontiers alike.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220100323
Funder
Australian Research Council
Funding Amount
$417,505.00
Summary
I can't find the word! Reading to maintain communication skills in ageing. This project aims to investigate why, as we age, we have trouble retrieving words when we speak but not when we read aloud. It takes the novel approach of systematically testing both reading and speaking in the same older adults. Through its innovative use of both behavioural research and computational modelling, it will generate new knowledge in spoken word production and reading, areas in which the project team have ack ....I can't find the word! Reading to maintain communication skills in ageing. This project aims to investigate why, as we age, we have trouble retrieving words when we speak but not when we read aloud. It takes the novel approach of systematically testing both reading and speaking in the same older adults. Through its innovative use of both behavioural research and computational modelling, it will generate new knowledge in spoken word production and reading, areas in which the project team have acknowledged expertise. This project will advance theories, achieving understanding of how ageing affects the cognitive systems involved in saying words and reading them aloud. By also investigating whether reading aloud can support word retrieval, it has potential future benefit for improved communication in older adults.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100149
Funder
Australian Research Council
Funding Amount
$462,044.00
Summary
Adaptive and Efficient Robot Positioning Through Model and Task Fusion. This project aims to create fit-for-purpose positioning systems that continuously adapt to diverse and changing environments. The project expects to contribute to the knowledge across robotics, computer vision, and neuromorphic computing. Expected outcomes of this project include ground-breaking place recognition techniques that address two fundamental limitations in the state-of-the-art: continuous adaptation, critically im ....Adaptive and Efficient Robot Positioning Through Model and Task Fusion. This project aims to create fit-for-purpose positioning systems that continuously adapt to diverse and changing environments. The project expects to contribute to the knowledge across robotics, computer vision, and neuromorphic computing. Expected outcomes of this project include ground-breaking place recognition techniques that address two fundamental limitations in the state-of-the-art: continuous adaptation, critically important in safety-critical systems, and energy efficiency, critically important in resource-constrained systems. This should provide significant benefits, such as accelerated deployment of mobile robots, drones and augmented reality solutions in manufacturing, defence, healthcare, household, and space.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE170101259
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Zero-shot and few-shot learning with deep knowledge transfer. This project aims to develop few-shot and zero-shot learning, visual recognition techniques that can learn a visual concept with few or no visual examples. Visual recognition is a major component in Artificial Intelligence and used in cybernetic security, robotic vision and medical image analysis. This project will use deep learning to enable the zero/few-shot learning to use and model previously unexplored information, making zero/fe ....Zero-shot and few-shot learning with deep knowledge transfer. This project aims to develop few-shot and zero-shot learning, visual recognition techniques that can learn a visual concept with few or no visual examples. Visual recognition is a major component in Artificial Intelligence and used in cybernetic security, robotic vision and medical image analysis. This project will use deep learning to enable the zero/few-shot learning to use and model previously unexplored information, making zero/few-shot learning more practical, scalable and flexible. The project is expected to advance the applicability of visual recognition in many challenging scenarios and provide effective tools to analyse the online visual data for supporting Australia’s cybernetic security.Read moreRead less