Discovery Early Career Researcher Award - Grant ID: DE170101081
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Adaptive value-flow analysis to improve code reliability and security. This project aims to develop client-driven adaptive value-flow analysis to detect software bugs in system software written in the C/C++ programme language. Static analysis tools for automated code inspections can benefit software developers, but are imprecise, inefficient and not user-friendly for analysing real-world industrial-sized software. The project will investigate static, dynamic and user-guided value-flow analysis t ....Adaptive value-flow analysis to improve code reliability and security. This project aims to develop client-driven adaptive value-flow analysis to detect software bugs in system software written in the C/C++ programme language. Static analysis tools for automated code inspections can benefit software developers, but are imprecise, inefficient and not user-friendly for analysing real-world industrial-sized software. The project will investigate static, dynamic and user-guided value-flow analysis to efficiently and precisely analyse large-scale programs according to clients’ needs, thereby allowing compilers to generate safe, reliable and secure code. This project is expected to advance value-flow analysis for industrial-sized software, improve software reliability and security, and benefit Australian software systems and industries.Read moreRead less
Programming Paradigms, Tools and Algorithms for Electronic Structure Calculations on Clusters of Non-Uniform Memory Access Parallel Processors. In recent years Australian academia has invested heavily in high performance computing systems. A significant fraction of these resources are devoted to performing computational chemistry studies, such as those used in drug design. This project links Australian researchers with the company responsible for a particularly widely used computational chemistr ....Programming Paradigms, Tools and Algorithms for Electronic Structure Calculations on Clusters of Non-Uniform Memory Access Parallel Processors. In recent years Australian academia has invested heavily in high performance computing systems. A significant fraction of these resources are devoted to performing computational chemistry studies, such as those used in drug design. This project links Australian researchers with the company responsible for a particularly widely used computational chemistry application package, and also with a major international computer company. Our aim is to substantially improve the performance of this code on cluster based compute systems. This, as well as our generic performance evaluation tools, would be of substantial benefit to the Australian research community. The project will forge links with researchers in Singapore, Japan and the USA.Read moreRead less
Towards High-performance and Fault-tolerant Distributed Java Implementations. Java Virtual Machines form an important part of the web and business
server market. Distributed Java Virtual Machines have the potential to
make a significant contribution to industries that utilize this
technology. An attractive platform for this purpose is the
cluster, a highly cost-effective and scalable parallel computer
model. However, realizing on such a platform a high performance virtual
machine implem ....Towards High-performance and Fault-tolerant Distributed Java Implementations. Java Virtual Machines form an important part of the web and business
server market. Distributed Java Virtual Machines have the potential to
make a significant contribution to industries that utilize this
technology. An attractive platform for this purpose is the
cluster, a highly cost-effective and scalable parallel computer
model. However, realizing on such a platform a high performance virtual
machine implementation tolerant to hardware and software faults, and
having efficient memory utilization, presents many challenging research
issues. This project will address these issues by extending a highly
efficient and extensible Java implementation to be aware of its cluster
environment.
Read moreRead less
Asymptotic Geometric Analysis and Machine Learning. Phenomena in large dimensions appear in a number of domains of Mathematics and adjacent domains of science (e.g. Computer Science), dealing with functions of infinitely growing number of parameters. Here, we focus on several questions naturally linked to Asymptotic Geometric Analysis which have natural applications to Statistical Learning Theory. We intend to use geometric, probabilistic and combinatorial methods to investigate these problems, ....Asymptotic Geometric Analysis and Machine Learning. Phenomena in large dimensions appear in a number of domains of Mathematics and adjacent domains of science (e.g. Computer Science), dealing with functions of infinitely growing number of parameters. Here, we focus on several questions naturally linked to Asymptotic Geometric Analysis which have natural applications to Statistical Learning Theory. We intend to use geometric, probabilistic and combinatorial methods to investigate these problems, with an emphasis on modern tools in Empirical Processes Theory and the theory of Random Matrices.Read moreRead less
High Performance Runtimes for Next Generation Languages. X10 is a type-safe, memory-safe programming language. This project will help make X10 a viable choice for secure software on the next generation of computer architectures. The proposed project will contribute to a better understanding of the fundamental processes that advance knowledge and facilitate the development of technological innovations (a research priority goal). By addressing a key emerging problem and consolidating Australian- ....High Performance Runtimes for Next Generation Languages. X10 is a type-safe, memory-safe programming language. This project will help make X10 a viable choice for secure software on the next generation of computer architectures. The proposed project will contribute to a better understanding of the fundamental processes that advance knowledge and facilitate the development of technological innovations (a research priority goal). By addressing a key emerging problem and consolidating Australian-based expertise in this area, the project will also enhance Australia’s capacity in frontier technologies research.Read moreRead less
Programming Paradigms, Tools and Algorithms for the Spectral Solution of the Electronic Schroedinger Equation on Non-Uniform Memory Parallel Processors. We propose to develop software tools and methods that are appropriate for current and future generations of large scale shared memory computer systems. Our purpose is to enable a more productive utilization of these architectures for scientific computation. We will focus on algorithms for solving differential equations appropriate to quantum che ....Programming Paradigms, Tools and Algorithms for the Spectral Solution of the Electronic Schroedinger Equation on Non-Uniform Memory Parallel Processors. We propose to develop software tools and methods that are appropriate for current and future generations of large scale shared memory computer systems. Our purpose is to enable a more productive utilization of these architectures for scientific computation. We will focus on algorithms for solving differential equations appropriate to quantum chemistry. In particular an exciting new class of methods whose computational cost scales linearly with system size. Our goal is to develop scalable parallel implementations of these methods. If realized this will revolutionize computation, enabling first principles calculations on truly nanoscale systems, such as enzymes and molecular electronic devices.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
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.
Special Research Initiatives - Grant ID: SR0567658
Funder
Australian Research Council
Funding Amount
$100,000.00
Summary
A cross-disciplinary collaboration to develop a national system for real-time detection of Adverse Drug Reactions using linked Australian health data. Our aim is to use existing administrative health data in the evidence-based, cost-effective and privacy-respecting discovery of Adverse Drug Reactions. This research is of vital importance, since adverse reactions to medicines currently represent one of the leading causes of hospitalisation and death in Australia. In a groundbreaking collaboration ....A cross-disciplinary collaboration to develop a national system for real-time detection of Adverse Drug Reactions using linked Australian health data. Our aim is to use existing administrative health data in the evidence-based, cost-effective and privacy-respecting discovery of Adverse Drug Reactions. This research is of vital importance, since adverse reactions to medicines currently represent one of the leading causes of hospitalisation and death in Australia. In a groundbreaking collaboration, we have successfully shown that large linked, administrative data sets are sufficiently rich to enable discovery of adverse drug reactions, but our analytic tools are at an early developmental stage. The outcome of this project would be innovative, effective and sustainable analytic tools for the discovery of unexpected associations between drugs and medical events.Read moreRead less
Dynamic Cooperative Performance Optimizations. This project seeks to improve the reliability, security, and
performance of modern software systems. Security is a problem of such
scale that outbreaks of computer viruses etc. headline in major
financial newspapers. We approach the problem by addressing the key
performance problems that hold back the programming languagues widely
used for secure and reliable systems. By improving the reliability,
security and performance of computer system ....Dynamic Cooperative Performance Optimizations. This project seeks to improve the reliability, security, and
performance of modern software systems. Security is a problem of such
scale that outbreaks of computer viruses etc. headline in major
financial newspapers. We approach the problem by addressing the key
performance problems that hold back the programming languagues widely
used for secure and reliable systems. By improving the reliability,
security and performance of computer systems, this project will help
alleviate the millions of hours and dollars lost to inadvertent errors
and malicious software attacks. The project will give Australia an
international presence in a research area of great academic and
commercial importance.Read moreRead less