Fast effective clustering technologies for highly dynamic massive networks. Clustering is a fundamental data mining and analysis task. In an interconnected evolving world, friendships and information flows are modelled as large dynamic networks. Structural clustering and correlation clustering are important and well-studied approaches for static networks; for evolving networks, where links appear and disappear over time, we lack efficient techniques. Anticipated outcomes are new practical cluste ....Fast effective clustering technologies for highly dynamic massive networks. Clustering is a fundamental data mining and analysis task. In an interconnected evolving world, friendships and information flows are modelled as large dynamic networks. Structural clustering and correlation clustering are important and well-studied approaches for static networks; for evolving networks, where links appear and disappear over time, we lack efficient techniques. Anticipated outcomes are new practical clustering algorithms for dynamic networks – with performance guarantees of efficiency and clustering quality – and prototype software, guiding us to pick a good clustering. Expected benefits include better understanding of spread in evolving social networks, accelerating the software testing cycle, and improved topic detection.Read moreRead less
Understanding prokaryotic small proteins from context. Prokaryotic small proteins are increasingly recognised to play important biological roles but have been largely overlooked due to the lack of adequate tools to study them. This project aims to develop new methods to identify and predict the functions of small proteins from microbial communities by studying sequence patterns in their genomes. These predicted functions will be confirmed in the laboratory, leading to a catalogue of newly charac ....Understanding prokaryotic small proteins from context. Prokaryotic small proteins are increasingly recognised to play important biological roles but have been largely overlooked due to the lack of adequate tools to study them. This project aims to develop new methods to identify and predict the functions of small proteins from microbial communities by studying sequence patterns in their genomes. These predicted functions will be confirmed in the laboratory, leading to a catalogue of newly characterised small proteins from a diverse range of habitats and geographies. By creating new ways to study the role of small proteins in the global microbiome, we will provide the foundational knowledge required to leverage these proteins for use in biotechnology. Read moreRead less
Genome evolution & adaptation of the multinuclear wheat stripe rust fungus. Animals and plants package their genomes into a single nucleus within each cell. In contrast, millions of fungal species accommodate multiple nuclei containing individual haploid genomes. It is currently unknown what the evolutionary implications are for this unusual genome division into multiple nuclei. Here we explore the evolutionary consequences of genome division into multiple nuclei for the first time by applying c ....Genome evolution & adaptation of the multinuclear wheat stripe rust fungus. Animals and plants package their genomes into a single nucleus within each cell. In contrast, millions of fungal species accommodate multiple nuclei containing individual haploid genomes. It is currently unknown what the evolutionary implications are for this unusual genome division into multiple nuclei. Here we explore the evolutionary consequences of genome division into multiple nuclei for the first time by applying cutting edge genome biology tools and algorithms. The economically significant study system is the devastating wheat stripe rust fungus. This pathogen costs Australian farmers over $100 million a year. New understanding is expected to lead to better disease management, reduced fungicide applications, and increased yields.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230101329
Funder
Australian Research Council
Funding Amount
$432,355.00
Summary
Trading Privacy, Bandwidth and Accuracy in Algorithmic Machine Learning. This project aims to investigate the trade-offs between privacy, communication costs and accuracy of results when learning from users' sensitive data. The project intends to design faster and more accurate algorithms for a wide range of machine learning tasks by developing a novel and widely-applicable algorithmic framework. Expected outcomes of this project include new theoretical tools to guide the design of data-driven d ....Trading Privacy, Bandwidth and Accuracy in Algorithmic Machine Learning. This project aims to investigate the trade-offs between privacy, communication costs and accuracy of results when learning from users' sensitive data. The project intends to design faster and more accurate algorithms for a wide range of machine learning tasks by developing a novel and widely-applicable algorithmic framework. Expected outcomes of this project include new theoretical tools to guide the design of data-driven decision systems and rigorously analyse their performance and privacy guarantees. Privacy of individuals' information in data analytics pipelines is a key societal concern. This project should lead to significant benefits by strengthening privacy in these pipelines while also improving accuracy and cost-efficiency.Read moreRead less
Attribution of Machine-generated Code for Accountability. Machine-generated (or neural) code is usually produced by AI tools to speed up software development. However, such codes have recently raised serious security and privacy concerns. This project aims to attribute these codes to their generative models for accountability purposes. In the process, a series of new techniques are developed to differentiate between the codes generated by different models. The outcomes include analysis of neural ....Attribution of Machine-generated Code for Accountability. Machine-generated (or neural) code is usually produced by AI tools to speed up software development. However, such codes have recently raised serious security and privacy concerns. This project aims to attribute these codes to their generative models for accountability purposes. In the process, a series of new techniques are developed to differentiate between the codes generated by different models. The outcomes include analysis of neural code fingerprints, classification of neural codes, and theories to verify the correctness of code attribution. These will provide significant benefits, ranging from copyright protection to privacy preservation. This project is timely since currently the software community is pervasively using neural codes.Read moreRead less
Pathogenic Repeat Expansions In Ataxia: Advancing Gene Discovery And Genetic Diagnosis
Funder
National Health and Medical Research Council
Funding Amount
$645,205.00
Summary
Hereditary ataxia is a severe neurological disorder that results in impaired coordination and balance and affects 1 in 20,000 Australians. Ataxias are often caused by complex genetic mutations called repeat expansions (RE), which are difficult to detect. Therefore, genetic diagnosis of ataxia remains limited and poorly accessible, leading to a gap in clinical care. In this study, we will utilise modern advances in genetic sequencing technology to diagnose and discover ataxias caused by REs.
Discovery Early Career Researcher Award - Grant ID: DE230100178
Funder
Australian Research Council
Funding Amount
$453,913.00
Summary
Fast, lightweight and live nanopore sequencing analysis. This project aims to address limitations in nanopore sequencing (latest emerging technology in genomics) by applying advanced computational methods. This project expects to create new knowledge in bioinformatics and computer science through innovative approaches that leverage the live data streaming capability of nanopore devices to deliver results rapidly, or in real-time. Expected outcomes include improved, highly efficient analysis meth ....Fast, lightweight and live nanopore sequencing analysis. This project aims to address limitations in nanopore sequencing (latest emerging technology in genomics) by applying advanced computational methods. This project expects to create new knowledge in bioinformatics and computer science through innovative approaches that leverage the live data streaming capability of nanopore devices to deliver results rapidly, or in real-time. Expected outcomes include improved, highly efficient analysis methods and designs for future creation of custom computer hardware for nanopore analysis. This will facilitate widespread adoption of nanopore technology in bioscience research and applied domains (health, agriculture, ecology, biosecurity and forensics), including for portable in-the-field applications. Read moreRead less
Trisections, triangulations and the complexity of manifolds. This project aims at practical representations of 3-dimensional and 4-dimensional spaces as needed in applications. Topology is the mathematical study of the shapes of spaces. Geometry endows spaces with additional structure such as distance, angle and curvature. Special combinatorial structures, such as minimal triangulations, are often closely connected to geometric structures or topological properties. This project aims to construct ....Trisections, triangulations and the complexity of manifolds. This project aims at practical representations of 3-dimensional and 4-dimensional spaces as needed in applications. Topology is the mathematical study of the shapes of spaces. Geometry endows spaces with additional structure such as distance, angle and curvature. Special combinatorial structures, such as minimal triangulations, are often closely connected to geometric structures or topological properties. This project aims to construct computable invariants, connectivity results for triangulations, and algorithms to recognise fundamental topological properties and structures such as trisections and bundles.Read moreRead less
Approximate structures for efficient processing of data streams. This project aims to increase the volume of streamed data that can be handled on a low-powered device with limited memory. In finance, health, and transport, data arrives at enormous rates, and data-driven decisions must be made quickly. Likewise, to keep Australia secure, national agencies monitor and gather vast data sets. Increasingly, devices and monitors that have limited resources are making these decisions and they require c ....Approximate structures for efficient processing of data streams. This project aims to increase the volume of streamed data that can be handled on a low-powered device with limited memory. In finance, health, and transport, data arrives at enormous rates, and data-driven decisions must be made quickly. Likewise, to keep Australia secure, national agencies monitor and gather vast data sets. Increasingly, devices and monitors that have limited resources are making these decisions and they require computational techniques that run extremely efficiently. The project expects to develop and improve approximate data structures that operate in tight resource bounds. Anticipated outcomes are improved event recognition and dramatic speedup in analysis of streams in areas such as finance, health, transport, and urban data.Read moreRead less
Approximate algorithms and architectures for area efficient system design. This project aims to develop simpler but reliable image recognition systems that can run on low-cost, small-scale platforms, for use in driver monitoring system (DMS) applications. Cheaper reliable DMS will lead to wider availability of this technology to end users and improve safety of motor vehicles. This project will develop approximate algorithmic and circuit techniques, provide training for research students and buil ....Approximate algorithms and architectures for area efficient system design. This project aims to develop simpler but reliable image recognition systems that can run on low-cost, small-scale platforms, for use in driver monitoring system (DMS) applications. Cheaper reliable DMS will lead to wider availability of this technology to end users and improve safety of motor vehicles. This project will develop approximate algorithmic and circuit techniques, provide training for research students and build capability in the area of approximate computing. It is also expected to lead to commercial products, licences and revenue, which will enable new job creation.
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