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Discovery Early Career Researcher Award - Grant ID: DE210101808
Funder
Australian Research Council
Funding Amount
$395,775.00
Summary
Genetic Programming for Big Data Analytics. The project aims to extend a powerful machine learning method, called genetic programming and also developing a new concept called Alpha program, for big data analytics. This project expects to generate a new approach by finding a systematic approach to develop gene structures using information theory. By borrowing the best genes from the population of programs, the Alpha program concept will be developed for the first time. The proposed approach aims ....Genetic Programming for Big Data Analytics. The project aims to extend a powerful machine learning method, called genetic programming and also developing a new concept called Alpha program, for big data analytics. This project expects to generate a new approach by finding a systematic approach to develop gene structures using information theory. By borrowing the best genes from the population of programs, the Alpha program concept will be developed for the first time. The proposed approach aims to enhance genetic programming for many practical problems. I contend that not only finding better tools for big data analytics is in the best interest of machine learning and big data communities, it also provides significant benefits for other communities and industries in Australia.Read moreRead less
Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, ....Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, statistics and different ways of aggregating information. By building a more complete theoretical map it is expected that new machine learning methods will be developed, but more importantly that machine learning will be able to be better integrated into larger socio-technical systems.Read moreRead less
Machine learning in adversarial environments. Machine learning underpins the technologies driving the economies of both Silicon Valley and Wall Street, from web search and ad placement, to stock predictions and efforts in fighting cybercrime. This project aims to answer the question: How can machines learn from data when contributors act maliciously for personal gain?
Footprints in instance space: visualising the suitability of optimisation algorithms. Optimisation problems underpin the efficiency and effectiveness of many critical sectors (e.g., healthcare, manufacturing, and defence). The project will provide both practitioners and researchers with powerful new tools to develop a much more robust understanding of the strengths and weaknesses of a variety of optimisation algorithms.
Discovery Early Career Researcher Award - Grant ID: DE170101134
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Feasible algorithms for big inference. This project aims to develop algorithms for computationally-intensive statistical tools to analyse Big Data. Big Data is ubiquitous in science, engineering, industry and finance, but needs special machine learning to conduct correct inferential analysis. Computational bottlenecks make many tried-and-true tools of statistical inference inadequate. This project will develop tools including false discovery rate control, heteroscedastic and robust regression an ....Feasible algorithms for big inference. This project aims to develop algorithms for computationally-intensive statistical tools to analyse Big Data. Big Data is ubiquitous in science, engineering, industry and finance, but needs special machine learning to conduct correct inferential analysis. Computational bottlenecks make many tried-and-true tools of statistical inference inadequate. This project will develop tools including false discovery rate control, heteroscedastic and robust regression and mixture models, via Big Data-appropriate optimisation and composite-likelihood estimation. It will make open, well-documented, and accessible software available for the scalable and distributable analysis of Big Data. The expected outcome is a suite of scalable algorithms to analyse Big Data.Read moreRead less
Security and Privacy of Individual Data Used to Extract Public Information. The project aims to contribute to the development of techniques to allow the harvesting of useful information without compromising personal privacy. Intelligent analysis of personal data can reveal valuable knowledge about a population but at a risk of invading an individual's privacy. This project aims to provide at least partial solutions to some of the problems associated with the protection of private data. In partic ....Security and Privacy of Individual Data Used to Extract Public Information. The project aims to contribute to the development of techniques to allow the harvesting of useful information without compromising personal privacy. Intelligent analysis of personal data can reveal valuable knowledge about a population but at a risk of invading an individual's privacy. This project aims to provide at least partial solutions to some of the problems associated with the protection of private data. In particular, it plans to work on the problem of security of statistical databases and privacy of streaming data. This would be underpinned by a study of anonymisation and homomorphic encryption. The expected outcomes are new theoretical results, new algorithms and protocols applicable to at least some of the current significant problems in information security.Read moreRead less
Rating and ranking sports players and teams using minimum message length. All sorts of games and sports could use better systems for rating and ranking teams. This is as true in sports-mad Australia as any other country. Improved and more accessible rating systems across a variety of activities should encourage the general public to take a greater interest in the mathematics, statistics, information theory and machine learning behind the systems. With Cadability as our Australia-based interna ....Rating and ranking sports players and teams using minimum message length. All sorts of games and sports could use better systems for rating and ranking teams. This is as true in sports-mad Australia as any other country. Improved and more accessible rating systems across a variety of activities should encourage the general public to take a greater interest in the mathematics, statistics, information theory and machine learning behind the systems. With Cadability as our Australia-based international industry partner, the global use of these systems will be to Australia's economic advantage. Having a more accurate rating system which is wider-reaching both in the number of sports and games and the number of participants per sport and game should also encourage greater participation from the general public.Read moreRead less
Prognosis based network-type feature extraction for complex biological data. This project aims to develop statistical tools that integrate high-throughput molecular data with biological knowledge to make discoveries in complex diseases. This project uses machine learning methods, statistical models and proteomic platforms to identify relationships among clinico-pathologic and molecular measurements. It will produce tools and insights that are intended to accelerate the process of biologically an ....Prognosis based network-type feature extraction for complex biological data. This project aims to develop statistical tools that integrate high-throughput molecular data with biological knowledge to make discoveries in complex diseases. This project uses machine learning methods, statistical models and proteomic platforms to identify relationships among clinico-pathologic and molecular measurements. It will produce tools and insights that are intended to accelerate the process of biologically and clinically significant discoveries in biomedical research. This project will help Australian researchers in statistics and users of statistics (from fields as diverse as biology, ecology, medicine, finance, agriculture and the social sciences) to make better predictions that are easier to understand.Read moreRead less
Micro-panel data with non-linear error components. This project aims to develop methods for panel data models with heterogeneous marginal effects and discrete choice outcomes, controlling for unobserved common factors and nonlinear error components; and apply the methodologies to analyse alcohol-fuelled violence and drug-related harm in Australia. The project lies at the forefront of advances in econometrics, and the outcomes are expected to broaden and deepen Australia’s knowledge base. Empiric ....Micro-panel data with non-linear error components. This project aims to develop methods for panel data models with heterogeneous marginal effects and discrete choice outcomes, controlling for unobserved common factors and nonlinear error components; and apply the methodologies to analyse alcohol-fuelled violence and drug-related harm in Australia. The project lies at the forefront of advances in econometrics, and the outcomes are expected to broaden and deepen Australia’s knowledge base. Empirical outcomes should inform and evaluate evidence-based policy interventions for crime prevention, and influence policy making about public transport and economic growth.Read moreRead less
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