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.
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.
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.
Mining multi-typed and dynamic graphs. Large volumes of data collected nowadays from real-world applications are often represented as graphs. The nodes and the edges of such graphs represent different types of entities and interactions, and they have time information. This project will develop algorithms that mine efficiently such multi-typed and dynamic graphs.
Scalable biocomputing on networks: design and mathematical foundations. This project aims to develop technology with the potential to disrupt computation by providing a way to solve combinatorial mathematical problems in an efficient manner. Electronic computers have revolutionised our lives over the last half-century, but there are tasks they can not do, usually those requiring multi-tasking, much as our brains do. This project aims to overcome some of these problems by physically using molecul ....Scalable biocomputing on networks: design and mathematical foundations. This project aims to develop technology with the potential to disrupt computation by providing a way to solve combinatorial mathematical problems in an efficient manner. Electronic computers have revolutionised our lives over the last half-century, but there are tasks they can not do, usually those requiring multi-tasking, much as our brains do. This project aims to overcome some of these problems by physically using molecular parts of living things moving within specially mathematically designed networks to solve, in parallel, "combinatorial" mathematical problems that vex traditional computers, while using far less energy than electronic devices. This project expects to develop this nascent field into a practically useful, disruptive technology based in Australia.Read moreRead less
Homomorphic cryptography: computing on encrypted data. This project is driven by the groundbreaking applications of a new cryptographic technology that allows analysis of encrypted (scrambled) data without needing to decrypt (unscramble) it first. The results of this project can be used to enable secure remote data storage, electronic auctions and voting, and protecting medical records.
Algorithms for hard graph problems based on auxiliary data. When solving computational problems, algorithms usually access only the data that is absolutely necessary to define the problem. However, much more data is often readily available. Especially for important or slowly evolving data, such as road networks, social graphs, company rankings, or molecules, more and more auxiliary data becomes available through computational processes, sensors, and simple user entries. This auxiliary data can g ....Algorithms for hard graph problems based on auxiliary data. When solving computational problems, algorithms usually access only the data that is absolutely necessary to define the problem. However, much more data is often readily available. Especially for important or slowly evolving data, such as road networks, social graphs, company rankings, or molecules, more and more auxiliary data becomes available through computational processes, sensors, and simple user entries. This auxiliary data can greatly speed up an algorithm and improve its accuracy. This project aims to design improved algorithms that harness auxiliary data to solve selected high-impact NP-hard graph problems, and will build a new empowering theory to discern when auxiliary data can be used to improve algorithms.Read moreRead less
Novel data mining techniques for complex network analysis and control. This project will develop novel data mining theories and algorithms to analyse complex networks for safe information publishing and sharing across networks. It will enable smart information use in bioinformatics, social science and business intelligence, help protect against cybercrime and promote Australia's international research profile.
Algorithms and data structures to support automated analysis of trajectory data. The emergence of a variety of tracking devices, surveillance systems and even electronic transaction and phone networks has resulted in the production of large amounts of positional information for vehicles, people and animals. The aim of the project is to develop tools that support automated analysis of such data sets.
Supra-classical quantum simulation in physically restricted models of quantum computation. Quantum computation evolved from the revolutionary twentieth century theories of Quantum Mechanics and Computer Science, offering computational power that potentially transcends traditional computing models. This project will accelerate the delivery of the promised benefits of quantum computation through advancing the theory of quantum simulation.