Asymptotic Geometric Analysis and Learning Theory. Learning Theory is used in various real-world applications in diverse research areas, ranging from Biology (e.g. DNA sequencing) to Information Sciences. Therefore, having a deep understanding of fundamental questions in Learning Theory, and in particular, pin-pointing the parameters that make a learning problem hard would have a significant practical impact. This projects aims to achieve this goal, and in addition, we expect it would have a hig ....Asymptotic Geometric Analysis and Learning Theory. Learning Theory is used in various real-world applications in diverse research areas, ranging from Biology (e.g. DNA sequencing) to Information Sciences. Therefore, having a deep understanding of fundamental questions in Learning Theory, and in particular, pin-pointing the parameters that make a learning problem hard would have a significant practical impact. This projects aims to achieve this goal, and in addition, we expect it would have a high theoretical value, as the questions we shall address are of independent interest to pure mathematicians.Read moreRead less
Geological sequestration of carbon dioxide in deep saline aquifers: coupled flow-mechanical considerations. Deep saline aquifers have been routinely proposed as sites for long-term, large-scale storage of carbon dioxide (CO2) emissions, as an option to assist the abatement of global warming. This project investigates expected engineering behaviour of deep saline aquifer reservoirs and their stability following CO2 sequestration.
Asymptotic Geometric Analysis and Learning Theory. Learning Theory is used in various real-world applications in diverse research areas, ranging from Biology (e.g. DNA sequencing) to Information Sciences. Therefore, having a deep understanding of fundamental questions in Learning Theory, and in particular, pin-pointing the parameters that make a learning problem hard would have a significant practical impact. This projects aims to achieve this goal, and in addition, we expect it would have a hig ....Asymptotic Geometric Analysis and Learning Theory. Learning Theory is used in various real-world applications in diverse research areas, ranging from Biology (e.g. DNA sequencing) to Information Sciences. Therefore, having a deep understanding of fundamental questions in Learning Theory, and in particular, pin-pointing the parameters that make a learning problem hard would have a significant practical impact. This projects aims to achieve this goal, and in addition, we expect it would have a high theoretical value, as the questions we shall address are of independent interest to pure mathematicians.
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Lead-free oxide perovskites for highly efficient solar cells. This project aims to develop nanostructured lead-free oxide perovskites for solar energy applications. These materials will strengthen the future of photovoltaic technology by overcoming bandgap voltage limitations and toxicity/stability issues that plague conventional silicon-based and emerging halide perovskite-based solar cells. This project is expected to advance the rational design of solar cells based on oxide perovskites, which ....Lead-free oxide perovskites for highly efficient solar cells. This project aims to develop nanostructured lead-free oxide perovskites for solar energy applications. These materials will strengthen the future of photovoltaic technology by overcoming bandgap voltage limitations and toxicity/stability issues that plague conventional silicon-based and emerging halide perovskite-based solar cells. This project is expected to advance the rational design of solar cells based on oxide perovskites, which are efficient, high output voltage, environmentally friendly photovoltaic technology Success of the proposed programme paves the way to promote photovoltaic technology as a mainstream power generation source and a significant contributor to achieving energy, environmental and economic goals.Read moreRead less
Geometric parameters in Learning Theory. We aim to investigate the behaviour of geometric parameters which appear naturally in Statistical Learning Theory. Those parameters are used to control the sample complexity, which is the size of a random sample needed to produce an accurate prediction. They are also of independent interest in the local theory of Banach spaces. We shall use geometric methods originating in the local theory of Banach spaces to investigate the parameters and the way they in ....Geometric parameters in Learning Theory. We aim to investigate the behaviour of geometric parameters which appear naturally in Statistical Learning Theory. Those parameters are used to control the sample complexity, which is the size of a random sample needed to produce an accurate prediction. They are also of independent interest in the local theory of Banach spaces. We shall use geometric methods originating in the local theory of Banach spaces to investigate the parameters and the way they influence sample complexity. All the problems we focus on are not only important from the Machine Learning point of view, but are intriguing in their theoretical implications.Read moreRead less
Using data mining methods to remove uncertainties in sensor data streams. This project will develop key techniques for removing uncertainties in sensor data streams and thus improve the monitoring quality of sensor networks. The expected outcomes will benefit Australia by enabling improved, lower-cost monitoring of natural resources and management of stock raising.
Reconceiving Machine Learning. The proposed research will develop a new way to consider problems to which machine learning can be applied. Machine learning is crucial enabler of the digital economy. The research will provide better opportunities for Australian industry to gain a competitive advantage with machine learning technology. The framework developed will enable better opportunities for collaborative research and will build and strengthen international linkages.
High-performance computational data-mining techniques for feature detection in complex time series from large-scale, networked plasma experiments. Terabytes of data are gathered from large experimental facilities as complex time-series. Analysis of these data is daunting, especially when they involve high-dimensional spectral or image arrays. We will develop high-performance computational techniques for dimension reduction, efficient data-mining, and experimental control, using as an initial ta ....High-performance computational data-mining techniques for feature detection in complex time series from large-scale, networked plasma experiments. Terabytes of data are gathered from large experimental facilities as complex time-series. Analysis of these data is daunting, especially when they involve high-dimensional spectral or image arrays. We will develop high-performance computational techniques for dimension reduction, efficient data-mining, and experimental control, using as an initial target the H-1NF plasma fusion MNRF at the ANU and its >100 GB/year data stream. The techniques will immediately provide Australian researchers with unique tools for collaboration in international research to develop fusion as a low-emissions source of electricity, and will be applicable to complex time-series analysis in other areas of science, medicine, and defence.Read moreRead less
Multifunctional trilayer separator for durable multivalent energy storage. This project aims to develop an important new family of economical, high energy, multivalent batteries based on an abundant element, sulphur. The project plans to design a new battery separator to enable long-term stability in sulphur-based rechargeable batteries. This type of separator is of critical importance in many membrane-involved energy storage technologies. The project plans to use leading-edge durable energy tec ....Multifunctional trilayer separator for durable multivalent energy storage. This project aims to develop an important new family of economical, high energy, multivalent batteries based on an abundant element, sulphur. The project plans to design a new battery separator to enable long-term stability in sulphur-based rechargeable batteries. This type of separator is of critical importance in many membrane-involved energy storage technologies. The project plans to use leading-edge durable energy technologies to strengthen the development of residential energy systems and the involvement of renewable energy sources in modern grid.Read moreRead less
Network Intrusion Detection via Machine Learning. Computer security is an increasingly important, yet complex task. It
takes significant skills to configure systems properly such that they
are safe from malicious attacks.
The proposed project aims at designing automatic systems which are
able to adapt to an existing network configuration and which detect
novel and unusual events. For this purpose we will use modern machine
learning techniques, mainly based on kernels. In particular, rec ....Network Intrusion Detection via Machine Learning. Computer security is an increasingly important, yet complex task. It
takes significant skills to configure systems properly such that they
are safe from malicious attacks.
The proposed project aims at designing automatic systems which are
able to adapt to an existing network configuration and which detect
novel and unusual events. For this purpose we will use modern machine
learning techniques, mainly based on kernels. In particular, recently
developed algorithms to estimate the support of a distribution and
detect rare events will be employed in this context.
The project is in cooperation with Dr. Ralf Herbrich (Microsoft
Research, Cambridge).
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