Deep correction of DNA sequencing errors by data mining algorithms. This project aims to investigate the many layers of error correction problems in the terabytes of genomic sequence data, and aims to solve these problems by novel data mining algorithms. High-throughput sequencing platforms have generated massive amounts of useful raw data, but also made widespread errors. The new algorithms are capable of correcting errors at deeper layers to further enhance data quality. Expected outcome inclu ....Deep correction of DNA sequencing errors by data mining algorithms. This project aims to investigate the many layers of error correction problems in the terabytes of genomic sequence data, and aims to solve these problems by novel data mining algorithms. High-throughput sequencing platforms have generated massive amounts of useful raw data, but also made widespread errors. The new algorithms are capable of correcting errors at deeper layers to further enhance data quality. Expected outcome includes the knowledge advancement of genomic data industry and interdisciplinary collaboration between biotechnology and data mining. This also provides significant benefit for genomic decisions in forensics and personalised medicine which demand accurate genomic information.Read moreRead less
Efficient data mining methods for evidence-based decision making. This project aims to develop efficient data mining methods for causal predictions. Evidence-based decision making (EBD), such as evidence-based medicine and policy, is always preferable. To support EBD, causal predictions forecast how outcomes change when conditions are manipulated. Progress has been made in theoretical research on causal inference based on observational data, but few methods can automatically mine causal signals ....Efficient data mining methods for evidence-based decision making. This project aims to develop efficient data mining methods for causal predictions. Evidence-based decision making (EBD), such as evidence-based medicine and policy, is always preferable. To support EBD, causal predictions forecast how outcomes change when conditions are manipulated. Progress has been made in theoretical research on causal inference based on observational data, but few methods can automatically mine causal signals from the data and methods for efficient causal predictions based on data are even fewer. This project will apply its methods to biomedical problems. The outcomes could support smart and data-driven evidence based decision making in many areas, such as therapeutics and government policy making.Read moreRead less
Reconstructing proteins to explain and engineer biological diversity. The aim of this project is to develop computational methods to construct entirely new proteins. Computational reconstruction of enzymes that have been extinct for over 400 million years has revealed remarkable opportunities for biotechnological innovation. The intended outcomes are to develop bioinformatics methods to broaden the scope of ancestral protein reconstruction to include protein super-families, to establish what spe ....Reconstructing proteins to explain and engineer biological diversity. The aim of this project is to develop computational methods to construct entirely new proteins. Computational reconstruction of enzymes that have been extinct for over 400 million years has revealed remarkable opportunities for biotechnological innovation. The intended outcomes are to develop bioinformatics methods to broaden the scope of ancestral protein reconstruction to include protein super-families, to establish what specific changes led to the evolutionary success of a protein, and to re-run evolution to generate proteins that perform in conditions suitable for industrial and agricultural applications, in particular the production of hydroxylated fatty acids for bioplastics. By examining proteins from many life forms, the project plans to develop a novel bioinformatics strategy to understand their evolution and engineer new proteins for use in production of chemical commodities.Read moreRead less
Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an ....Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an open-source tool that can capture precision correlations between deep code features and diverse vulnerabilities to pinpoint emerging vulnerabilities without the need for bug specifications. Significant benefits include greatly improved quality, reliability and security for modern software systems.Read moreRead less
Finding and exploiting interesting paths in multidimensional information spaces. This project will invent a new approach for searching within a large complex information space, finding interesting paths between points within the space, visualising the results, and supporting rich, human-centric user interaction with queries and results. This project will embody these techniques in a novel, internet-scale framework to support rapid development of large path search and visualisation applications. ....Finding and exploiting interesting paths in multidimensional information spaces. This project will invent a new approach for searching within a large complex information space, finding interesting paths between points within the space, visualising the results, and supporting rich, human-centric user interaction with queries and results. This project will embody these techniques in a novel, internet-scale framework to support rapid development of large path search and visualisation applications. Evaluation will be via development of several exemplar applications. The techniques and framework will be applicable to a broad range of economically important problems in areas as diverse as health, travel, scientific publication search, product marketing and software engineering.Read moreRead less
Mining large negative correlations for high-dimensional contrasting analysis. Negative correlations are widely embedded in real life applications, but in-depth research has rarely been conducted due to its high level of complexity. This project aims at efficient algorithms and frontier theory for finding large negative correlations, to enable smart information use in bioinformatics to promote Australia's leading role in data mining research.
Decentralised Data Management for Edge Caching Systems in 5G. This project aims to deliver a suite of decentralised data management approaches to facilitate practical edge caching systems in the 5G mobile edge computing (MEC) environment. Edge caching offers great promises for Australia's post-COVID economic recovery and resilience with the ability to enable real-time mobile and IoT software applications in various domains, e.g., telehealth, online learning/working, advanced manufacturing, etc. ....Decentralised Data Management for Edge Caching Systems in 5G. This project aims to deliver a suite of decentralised data management approaches to facilitate practical edge caching systems in the 5G mobile edge computing (MEC) environment. Edge caching offers great promises for Australia's post-COVID economic recovery and resilience with the ability to enable real-time mobile and IoT software applications in various domains, e.g., telehealth, online learning/working, advanced manufacturing, etc. This project tackles new and urgent challenges in edge data storage, manipulation, maintenance, and protection with optimisation, distributed consensus, graph analytics, and cryptography techniques. The outcomes should build the pillars of edge caching systems and promote Australia's 5G software innovations.Read moreRead less
Secure user authentication with continuous adaptive risk evaluation. Users typically authenticate to any given system only once - when they first access it (for example, through providing a password or fingerprint). The prevalence of single sign-on further allows this single authentication to be sufficient for access to multiple systems. Thus an adversary can obtain a large degree of access from stealing a single password, hijacking a user's session, or even simply borrowing their phone. This pr ....Secure user authentication with continuous adaptive risk evaluation. Users typically authenticate to any given system only once - when they first access it (for example, through providing a password or fingerprint). The prevalence of single sign-on further allows this single authentication to be sufficient for access to multiple systems. Thus an adversary can obtain a large degree of access from stealing a single password, hijacking a user's session, or even simply borrowing their phone. This project aims to develop a continuous authentication approach based on user behaviour - typical interactions plus biometrics (for example, keystroke dynamics) - combined with a risk adaptive assessment of the resources being accessed, resulting in re-authentication requests in the event of a suspected compromise.Read moreRead less
Virtual Environments for Improved Enterprise Software Deployment. This project aims to improve quality assurance for enterprise IT. Enterprise IT systems are highly interconnected and interdependent — a failure in one system can cause a cascade of failures across multiple systems, bringing business to a standstill. The project aims to create new technologies to automate the provisioning of virtual deployment environments to test the enterprise systems. In particular, it aims to develop new metho ....Virtual Environments for Improved Enterprise Software Deployment. This project aims to improve quality assurance for enterprise IT. Enterprise IT systems are highly interconnected and interdependent — a failure in one system can cause a cascade of failures across multiple systems, bringing business to a standstill. The project aims to create new technologies to automate the provisioning of virtual deployment environments to test the enterprise systems. In particular, it aims to develop new methods for the automatic analysis of service interaction traces and the generation of accurate executable service models, without requiring explicit knowledge of them. The automatic analysis and generation should reduce development cost for enterprise IT systems and increase system quality and reliability. The new software deployment technologies from this project aim to significantly reduce the time, effort and cost of system quality assurance activities in software development organisations, and yet produce higher-quality software leading to uninterrupted business operation in end-user organisations across all sectors.Read moreRead less
Next-generation techniques for analysing massive data sets. To process enormous amounts of data, leading computing companies are turning to modern computing frameworks, for which little theory of efficient computational techniques has been developed. This project will resolve key theoretical questions and provide fast techniques for poorly understood pattern recognition and bioinformatics problems.