Discovery Early Career Researcher Award - Grant ID: DE170100572
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Collaborative information seeking and its application in tourism. The project aims to better understand group behaviour of information seeking. Collaboration is an essential aspect of modern life; collaborative work, including tourism, encompasses obtaining and using information. However, most information behaviour models focus on the individual seeker of information, rather than on improving collaboration and team performance. The project builds models and develops guidelines for understanding ....Collaborative information seeking and its application in tourism. The project aims to better understand group behaviour of information seeking. Collaboration is an essential aspect of modern life; collaborative work, including tourism, encompasses obtaining and using information. However, most information behaviour models focus on the individual seeker of information, rather than on improving collaboration and team performance. The project builds models and develops guidelines for understanding and supporting collaborative information seeking behaviour in the context of tourism. Making it easier for tourists to find information is expected to benefit the Australian tourism industry.Read moreRead less
Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently ....Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently learn complex interaction and navigation behaviours that transfer to unseen environments. Our research should benefit new applications in domains of economic and societal importance that are currently too complex, unsafe, and uncertain for robot assistants, such as aged care, advanced manufacturing and domestic robotics.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC210100056
Funder
Australian Research Council
Funding Amount
$3,975,864.00
Summary
ARC Training Centre for Next-Gen Technologies in Biomedical Analysis . The Centre for Next-Gen Technologies in Biomedical Analysis will deliver workforce trained in the development of transformative technologies that will rapidly expand the Australian pharmaceutical, diagnostic and defence sector. The university-industry partnership will increase Australia’s manufacturing capability by fast tracking screening, by integrating 3D printing, advanced sensing, big data analytics, machine learning an ....ARC Training Centre for Next-Gen Technologies in Biomedical Analysis . The Centre for Next-Gen Technologies in Biomedical Analysis will deliver workforce trained in the development of transformative technologies that will rapidly expand the Australian pharmaceutical, diagnostic and defence sector. The university-industry partnership will increase Australia’s manufacturing capability by fast tracking screening, by integrating 3D printing, advanced sensing, big data analytics, machine learning and artificial intelligence for the delivery of optimal solutions in diagnosis, treatment and wellbeing. The centre will deliver training in Industry 4.0 skills which will boost early-stage scale-up and accelerate the sector’s supply chain, which is pivotal for the Australian industries to maintain a competitive edge. Read moreRead less
Deep Learning that Scales. Deep learning has dramatically improved the accuracy of a breathtaking variety of tasks in AI such as image understanding and natural language processing. This project addresses fundamental bottlenecks when attempting to develop deep learning applications at scale. First, this project proposes efficient neural architecture search that is orders of magnitude faster than previously reported, abstracting away the most complex part of deep learning. Second, we will desig ....Deep Learning that Scales. Deep learning has dramatically improved the accuracy of a breathtaking variety of tasks in AI such as image understanding and natural language processing. This project addresses fundamental bottlenecks when attempting to develop deep learning applications at scale. First, this project proposes efficient neural architecture search that is orders of magnitude faster than previously reported, abstracting away the most complex part of deep learning. Second, we will design very efficient binary networks, enabling large-scale deployment of deep learning to mobile devices. Thus this project will overcome two primary limitations of deep learning generally, however, and will greatly increase its already impressive domain of practical application.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC170100030
Funder
Australian Research Council
Funding Amount
$4,133,659.00
Summary
ARC Training Centre in Cognitive Computing for Medical Technologies. The ARC Training Centre in Cognitive Computing for Medical Technologies aims to create a workforce that is expert in developing, applying and interrogating cognitive computing technologies in data-intensive medical contexts. This will facilitate the next generation of data-driven and machine learning-based medical technologies. The Centre will provide a world-class industry-driven research training environment for PhD students ....ARC Training Centre in Cognitive Computing for Medical Technologies. The ARC Training Centre in Cognitive Computing for Medical Technologies aims to create a workforce that is expert in developing, applying and interrogating cognitive computing technologies in data-intensive medical contexts. This will facilitate the next generation of data-driven and machine learning-based medical technologies. The Centre will provide a world-class industry-driven research training environment for PhD students and postdoctoral researchers. These researchers will lead the medical technology industry into a new era of data-driven personalised and precision medical devices and applications. The Centre will result in the development of capabilities in the core technologies of machine learning and the practical application of cognitive computing in the area of health.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE130100156
Funder
Australian Research Council
Funding Amount
$210,000.00
Summary
Computational infrastructure for machine learning in computer vision. The many trillions of images stored on computers around the world, including more than 100 billion on Facebook alone, represent exactly the information needed to develop artificial vision. All we need do is extract it. This project will develop the computational infrastructure required to allow Australian researchers to achieve this goal.
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE180100158
Funder
Australian Research Council
Funding Amount
$348,026.00
Summary
A large-scale distributed experimental facility for the internet of things. This project aims to establish a large-scale, real-world experimental facility for the Internet of Things (IoT), which is currently missing in Australia, as well as in the rest of the world. The project is expected to be an essential instrument to achieve Australia’s leadership on key enabling technologies of the IoT, and to provide Australian research community with a unique platform for large-scale experimentation and ....A large-scale distributed experimental facility for the internet of things. This project aims to establish a large-scale, real-world experimental facility for the Internet of Things (IoT), which is currently missing in Australia, as well as in the rest of the world. The project is expected to be an essential instrument to achieve Australia’s leadership on key enabling technologies of the IoT, and to provide Australian research community with a unique platform for large-scale experimentation and evaluation of IoT technologies and services. The project will also serve as a vehicle for the education and training of Australia’s next generation of scholars and engineers, and contribute to Australia’s scientific visibility.Read moreRead less
Decomposition and Duality: New Approaches to Integer and Stochastic Integer Programming. Because of their rich modelling capabilities, integer programs are widely used in industry for decision making and planning. However their solution algorithms do not have the maturity of their cousins in convex optimisation, where the theory of strong duality is ubiquitous. Efficient methods for convex optimisation under uncertainty do not apply to the integer case, which is highly non-convex. Furthermore, i ....Decomposition and Duality: New Approaches to Integer and Stochastic Integer Programming. Because of their rich modelling capabilities, integer programs are widely used in industry for decision making and planning. However their solution algorithms do not have the maturity of their cousins in convex optimisation, where the theory of strong duality is ubiquitous. Efficient methods for convex optimisation under uncertainty do not apply to the integer case, which is highly non-convex. Furthermore, integer models usually assume the data is known with certainty, which is often not the case in the real world. This project will develop new theory and algorithms to enhance the analysis of integer models, including those that incorporating uncertainty, while also enabling the use of parallel computing paradigms. Read moreRead less
ARC Centre of Excellence for Gravitational Wave Discovery. This Centre aims to explore the historic first detections of gravitational waves to understand the extreme physics of black holes and warped spacetime, and inspire the next generation of Australian scientists and engineers. The next-generation gravity wave detectors will enable a thousand-fold increase in detection volume and result in the new gravitational wave discoveries, triggering a new era of gravitational wave astrophysics. Buil ....ARC Centre of Excellence for Gravitational Wave Discovery. This Centre aims to explore the historic first detections of gravitational waves to understand the extreme physics of black holes and warped spacetime, and inspire the next generation of Australian scientists and engineers. The next-generation gravity wave detectors will enable a thousand-fold increase in detection volume and result in the new gravitational wave discoveries, triggering a new era of gravitational wave astrophysics. Building on decades of Australian investment in gravitational wave and pulsar science, this Centre will coalesce research activities into a focussed national programme whose discoveries are intended to experimentally validate Einstein’s General Theory of Relativity and educate the public about the wonders of Einstein's Universe.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE160100850
Funder
Australian Research Council
Funding Amount
$330,000.00
Summary
Dynamic adaptive software configuration. The aim of this project is to use computational intelligence in software engineering processes to achieve self-optimising products. Many mobile device users bemoan inadequate battery life, and device size is largely determined by the battery. In addition, information and communications technology uses six per cent of the global electricity production. The expected outputs of this project will be packages of optimisation components that software engineers ....Dynamic adaptive software configuration. The aim of this project is to use computational intelligence in software engineering processes to achieve self-optimising products. Many mobile device users bemoan inadequate battery life, and device size is largely determined by the battery. In addition, information and communications technology uses six per cent of the global electricity production. The expected outputs of this project will be packages of optimisation components that software engineers can incorporate into next generation products: the products analyse their collected usage data, perform what-if analyses, and optimise their configurations accordingly for the next usage period. Hence, the products may respond faster, be more reliable, and consume less energy.Read moreRead less