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.
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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Parameterized Analysis of Bio-inspired Computing - From Theory to High Performing Algorithms. This project will establish the field of parameterised analysis of bio-inspired computing which includes prominent approaches such as evolutionary algorithms and ant colony optimisation. It will rigorously analyse features of instances of combinatorial optimisation problems and their impact on the runtime behaviour of bio-inspired computing methods. Furthermore, the project will design new bio-inspired ....Parameterized Analysis of Bio-inspired Computing - From Theory to High Performing Algorithms. This project will establish the field of parameterised analysis of bio-inspired computing which includes prominent approaches such as evolutionary algorithms and ant colony optimisation. It will rigorously analyse features of instances of combinatorial optimisation problems and their impact on the runtime behaviour of bio-inspired computing methods. Furthermore, the project will design new bio-inspired computing algorithms that make use of instance features and hardness characteristics. The results will advance the theoretical knowledge of bio-inspired computing, bridge the gap between theory and practice, and provide more powerful algorithms for complex optimisation problems occurring for example in the field of supply chain management for the mining industry.Read moreRead less
Data retrieval from massive information structures. Information search is an essential tool. But most current services regard the data as unstructured collections of independent documents, free of context. Next-generation search applications, such as over social networks, or corporate websites, or XML data sets, must account for the inherent relationships between data items, and must allow the efficient inclusion of search context. Queries should favour semantically local data, giving results th ....Data retrieval from massive information structures. Information search is an essential tool. But most current services regard the data as unstructured collections of independent documents, free of context. Next-generation search applications, such as over social networks, or corporate websites, or XML data sets, must account for the inherent relationships between data items, and must allow the efficient inclusion of search context. Queries should favour semantically local data, giving results that depend on the perceived state of the querier. This project will develop indexing and search techniques for massive structured data sets. The new search methods will incorporate theoretical advances and will be experimentally validated using industry-standard open-source distributed systems.Read moreRead less