Deep visual understanding: learning to see in an unruly world. Deep Learning has achieved incredible success at an astonishing variety of Computer Vision tasks recently. This project aims to convey this success into the challenging domain of high-level image-based reasoning. It will extend deep learning to achieve flexible semantic reasoning about the content of images based on information gleaned from the huge volumes of data available on the Internet. The project expects to overcome one of the ....Deep visual understanding: learning to see in an unruly world. Deep Learning has achieved incredible success at an astonishing variety of Computer Vision tasks recently. This project aims to convey this success into the challenging domain of high-level image-based reasoning. It will extend deep learning to achieve flexible semantic reasoning about the content of images based on information gleaned from the huge volumes of data available on the Internet. The project expects to overcome one of the primary limitations of deep learning and will greatly increase its practical application to a range of industrial, cultural or health settings.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190100539
Funder
Australian Research Council
Funding Amount
$408,000.00
Summary
Towards conversational vision-based Artificial Intelligence. This project aims to develop a novel learning framework, Vision-Ask-Answer-Act (V3A). This framework will allow a machine to perform a sequence of actions via a conversation with human users, based on intricate processing of not just visual input, but human-computer verbal exchanges. Artificial intelligence has great potential as a tool for economic productivity and daily tasks. Applications in cars and assistant robots, still in their ....Towards conversational vision-based Artificial Intelligence. This project aims to develop a novel learning framework, Vision-Ask-Answer-Act (V3A). This framework will allow a machine to perform a sequence of actions via a conversation with human users, based on intricate processing of not just visual input, but human-computer verbal exchanges. Artificial intelligence has great potential as a tool for economic productivity and daily tasks. Applications in cars and assistant robots, still in their early days, typically require significant expertise to use effectively. The outcomes of this project will push the boundary of vision-language research to produce a conversational intelligent agent that can be easily used in common situations across industry, transport, the medical sector, and at home.Read moreRead less
New biocatalysts for selective chemical oxidations under extreme conditions. This project will identify and design new enzyme biocatalysts which function under extreme conditions such as elevated temperature and high concentrations of peroxides. These enzymes will be sourced from microorganisms which are located in extreme biological environments e.g. hot springs (the so-called extremophiles). The expected outcome of this project are the identification of robust enzymes which can catalyse select ....New biocatalysts for selective chemical oxidations under extreme conditions. This project will identify and design new enzyme biocatalysts which function under extreme conditions such as elevated temperature and high concentrations of peroxides. These enzymes will be sourced from microorganisms which are located in extreme biological environments e.g. hot springs (the so-called extremophiles). The expected outcome of this project are the identification of robust enzymes which can catalyse selective oxidation reactions in complex organic molecules, such as steroids. The new biocatalysts developed in this project will have significant benefit in the development of new routes to access bespoke molecules of value in fine chemical synthesis and drug development.
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Discovery Early Career Researcher Award - Grant ID: DE240100502
Funder
Australian Research Council
Funding Amount
$424,875.00
Summary
Building Molecular Complexity Through Enzyme-Enabled Synthesis. Many valuable natural molecules are too complex to be commercially synthesised by current technologies. Despite advances in synthetic chemistry there is great need to adopt the elegant biocatalytic strategies for complex molecule synthesis found in nature, employing sophisticated enzyme catalysts. This interdisciplinary research program aims to address the shortcomings of traditional synthetic methods through the development of enzy ....Building Molecular Complexity Through Enzyme-Enabled Synthesis. Many valuable natural molecules are too complex to be commercially synthesised by current technologies. Despite advances in synthetic chemistry there is great need to adopt the elegant biocatalytic strategies for complex molecule synthesis found in nature, employing sophisticated enzyme catalysts. This interdisciplinary research program aims to address the shortcomings of traditional synthetic methods through the development of enzyme catalysts to rapidly generate complex molecular structures. These novel molecules can be readily converted into pharmaceuticals and agrochemicals leading to advancements in the bio-enabled production and application of organic molecules in these vital fields. Read moreRead less
Making Meta-learning Generalised . This project aims to develop novel machine learning techniques, termed generalised meta-learning, to make machines better utilise past experience to solve new tasks with few data. It expects to reduce the undesirable dependence of current machine learning on labelled data and significantly expand its application scope. Expected outcomes of the project consist of new theoretical results on meta-learning and a set of innovative algorithms that can support the bui ....Making Meta-learning Generalised . This project aims to develop novel machine learning techniques, termed generalised meta-learning, to make machines better utilise past experience to solve new tasks with few data. It expects to reduce the undesirable dependence of current machine learning on labelled data and significantly expand its application scope. Expected outcomes of the project consist of new theoretical results on meta-learning and a set of innovative algorithms that can support the building of next generation of computer vision systems to work in open and dynamic environments. This should be able to produce solid benefits to the science, society, and economy of Australian via the application of these advanced intelligent systems.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220100265
Funder
Australian Research Council
Funding Amount
$417,000.00
Summary
A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and ....A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and interpretability in decision making. Expected outcomes will benefit national cybersecurity by improving our understanding of vulnerabilities and threats involving decision actions, and by ensuring that human feedback and evaluations can help prevent catastrophic events in explorations of dynamic and complex environments.Read moreRead less
ARC Centre of Excellence in Quantum Biotechnology. ARC Centre of Excellence in Quantum Biotechnology. The ARC Centre of Excellence in Quantum Biotechnology aims to develop paradigm-shifting quantum technologies to observe biological processes and transform our understanding of life. It seeks to create technologies that go far beyond what is possible today, from portable brain imagers to super-fast single protein sensors, and to use them to unravel key problems including how enzymes catalyse reac ....ARC Centre of Excellence in Quantum Biotechnology. ARC Centre of Excellence in Quantum Biotechnology. The ARC Centre of Excellence in Quantum Biotechnology aims to develop paradigm-shifting quantum technologies to observe biological processes and transform our understanding of life. It seeks to create technologies that go far beyond what is possible today, from portable brain imagers to super-fast single protein sensors, and to use them to unravel key problems including how enzymes catalyse reactions and how higher brain function emerges from networks of neurons. By building a diverse, multidisciplinary, and industry-engaged ecosystem, the Centre means to develop our future leaders at the interface of quantum science and biology and drive Australian innovation across manufacturing, energy, agriculture, health, and national security.Read moreRead less
New Paradigms for Robust Fitting: Kernelisation and Polyhedral Search. Outliers inevitably exist in visual data due to imperfect data acquisition or preprocessing. To enable computer vision applications that can perform reliably, robust fitting algorithms are necessary to counter the biasing influence of outliers. However, current robust algorithms are unsatisfactory: they are unreliable (due to using randomisation) or too computationally costly (due to using exhaustive search). This project wil ....New Paradigms for Robust Fitting: Kernelisation and Polyhedral Search. Outliers inevitably exist in visual data due to imperfect data acquisition or preprocessing. To enable computer vision applications that can perform reliably, robust fitting algorithms are necessary to counter the biasing influence of outliers. However, current robust algorithms are unsatisfactory: they are unreliable (due to using randomisation) or too computationally costly (due to using exhaustive search). This project will develop new robust algorithms to mitigate these shortcomings. It will do so by investigating two new paradigms of kernelisation and polyhedral search, which offer unprecedented theoretical insights into the problem. The outcomes will contribute towards computer vision applications that are more practical and reliable.Read moreRead less
Collision Avoidance in Shipping Lanes via Intelligent Sensor Data Fusion . This project aims to develop an online maritime traffic monitoring system for reliable collision/contact avoidance that exploits complementary data from high-resolution airborne sensors and surface vessel sensors. Our approach is based on optimal scheduling and fusion of the sensor data and possibly other sources of data to construct a comprehensive dynamic picture of maritime traffic, in real-time. Moreover, the proposed ....Collision Avoidance in Shipping Lanes via Intelligent Sensor Data Fusion . This project aims to develop an online maritime traffic monitoring system for reliable collision/contact avoidance that exploits complementary data from high-resolution airborne sensors and surface vessel sensors. Our approach is based on optimal scheduling and fusion of the sensor data and possibly other sources of data to construct a comprehensive dynamic picture of maritime traffic, in real-time. Moreover, the proposed methodology enables quantification of confidence in the predictions. This will provide ship owners, directly to their vessels and/or at the fleet management centres, information such as weather reports, reliable collision/no-collision warnings and avoidance strategies, on-the-fly. Read moreRead less