Visual interaction methods for clustered graphs. This project aims to improve human understanding of huge network data sets, such as those arising in social networks, biological networks, and very large software structures. The project will enable analysts to explore and interact with such data sets, leading to better understanding.
Software-defined provisioning of Internet of Things applications in fog computing systems. This project aims to investigate and provide solutions for the realisation of a seemingly integrated Fog Computing (FC) paradigm with cloud environments, networking devices and Internet of Things devices. Fog Computing (FC) is an emerging paradigm with great promises for advancing Information and Communications Technologies. Using interdisciplinary approaches, the project expects to generate new knowledge ....Software-defined provisioning of Internet of Things applications in fog computing systems. This project aims to investigate and provide solutions for the realisation of a seemingly integrated Fog Computing (FC) paradigm with cloud environments, networking devices and Internet of Things devices. Fog Computing (FC) is an emerging paradigm with great promises for advancing Information and Communications Technologies. Using interdisciplinary approaches, the project expects to generate new knowledge for optimising both hardware and software resources of a FC system. Outcomes of this project include practical solutions through building novel mathematical frameworks and optimisation objectives. The project is expected to provide efficient monitoring and control of intelligent spaces, management of urban and rural environments and will have applications in the areas of energy, security, transport and public health.Read moreRead less
Local reoptimization for turbocharging heuristics. Theoretical computer science has up until now had little impact on the design of effective heuristics. While data sets may be large, significant structure is almost always present and important to take into account when designing algorithms. Parameterised complexity considers the underlying structure by parameterising not only on the size of the input but also on structural parameters. This project aims to take advantage of the many opportunitie ....Local reoptimization for turbocharging heuristics. Theoretical computer science has up until now had little impact on the design of effective heuristics. While data sets may be large, significant structure is almost always present and important to take into account when designing algorithms. Parameterised complexity considers the underlying structure by parameterising not only on the size of the input but also on structural parameters. This project aims to take advantage of the many opportunities for new theories in the design of new heuristics and in turbocharging existing heuristics for computationally hard problems.Read moreRead less
Algorithmic and computational advances in geometric group theory. This project aims to combine new algorithmic ideas, high performance computing and experimental mathematics to answer many outstanding questions in the field of geometric group theory. This project will put Australia at the forefront of new computer-assisted research, and give new insights into complex mathematical problems.
Discovery Early Career Researcher Award - Grant ID: DE130101664
Funder
Australian Research Council
Funding Amount
$357,084.00
Summary
Universal solution for scheduling problems. The aim of this project is to design efficient algorithms that compute universal solutions for scheduling on an unreliable machine. Such solutions are specially suitable for situations where machines can behave unpredictably, such as scheduling in cloud computing.
Algorithmics for visual analytics of massive complex networks. The project will provide new scalable algorithms for visual analytics of massive complex networks. These fast algorithms will enable security analysts to detect abnormal behaviours such as money laundering, biologists to understand protein-protein interaction networks, and support software engineers new ways of understanding large software systems.
Algorithms for geometric Turán-type problems and network visualization. Recent technological advances have large data sets, in a data deluge. Some of the most critical data sets are networks; examples abound in Systems Biology, Social Network Analysis, and Software Engineering. This project aims for algorithms to construct readable pictures of these networks, and thus make the data easier for humans to understand.
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
Identifying technological trajectories using machine learning algorithms. This project aims to improve our understanding of why scientific knowledge progresses in certain directions and what causes it to grow faster or slower across fields. The project will create new neural-network machine-learning algorithms to scan patent and scientific article texts (specifications and claims) for natural language concepts. The results will potentially be used by patent offices to improve their own database ....Identifying technological trajectories using machine learning algorithms. This project aims to improve our understanding of why scientific knowledge progresses in certain directions and what causes it to grow faster or slower across fields. The project will create new neural-network machine-learning algorithms to scan patent and scientific article texts (specifications and claims) for natural language concepts. The results will potentially be used by patent offices to improve their own database search, by business analytics companies to reveal new technologies and potential collaborators, and by academic economists to understand how knowledge travels and accumulates.
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