Rethinking the Data-driven Discovery of Rare Phenomena. This project will investigate novel technologies for the data-driven discovery of rare phenomena. Scientific disciplines are increasingly able to generate large amounts of data relevant to key discoveries such as novel photovoltaic materials or explanations of brain seizures. However, these discoveries typically correspond to extremely rare phenomena in high dimensional spaces, which current data science methods are unable to detect. The pr ....Rethinking the Data-driven Discovery of Rare Phenomena. This project will investigate novel technologies for the data-driven discovery of rare phenomena. Scientific disciplines are increasingly able to generate large amounts of data relevant to key discoveries such as novel photovoltaic materials or explanations of brain seizures. However, these discoveries typically correspond to extremely rare phenomena in high dimensional spaces, which current data science methods are unable to detect. The project will fill this void and yield novel methods, publications, and open source software for the data-driven discovery or rare phenomena. Thus, it will expand the capabilities of data science, providing better use of the massive data collections accumulating across science, government, and industry.Read moreRead less
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
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
Improving Productivity and Efficiency of Australian Airports – A Real Time Analytics and Statistical Approach. Aviation is a major economic driver both within Australia and overseas, but the aviation industry faces growing challenges from the increase in passengers and changing regulations. To meet these challenges, airports, airlines, government agencies and others need to maximise their efficiency and productivity; however, complex dependencies and differing operational objectives complicate t ....Improving Productivity and Efficiency of Australian Airports – A Real Time Analytics and Statistical Approach. Aviation is a major economic driver both within Australia and overseas, but the aviation industry faces growing challenges from the increase in passengers and changing regulations. To meet these challenges, airports, airlines, government agencies and others need to maximise their efficiency and productivity; however, complex dependencies and differing operational objectives complicate this task. This project aims to develop a real-time, whole-of-system operational performance framework that can help operators in finding and evaluating solutions to maximise throughput, reduce wait times and mitigate flow-on effects. Innovative new video analytic and Bayesian Network based tools are integrated to address the challenges of adaptability and uncertainty.Read moreRead less
Gravitational-wave astronomy: detection and beyond. This project aims to detect ripples in the fabric of spacetime known as gravitational waves by using new data analysis techniques while developing technology to enable the next generation of gravitational-wave detectors. Detection of gravitational waves would constitute a revolution in astronomy, allowing us to probe the most dramatic events in the Universe with a new form of radiation. During the next five years, it is probable that gravitatio ....Gravitational-wave astronomy: detection and beyond. This project aims to detect ripples in the fabric of spacetime known as gravitational waves by using new data analysis techniques while developing technology to enable the next generation of gravitational-wave detectors. Detection of gravitational waves would constitute a revolution in astronomy, allowing us to probe the most dramatic events in the Universe with a new form of radiation. During the next five years, it is probable that gravitational waves will be detected. Terrestrial detectors, operating in the audio band, and pulsar timing arrays, operating in the nanohertz band, are both rapidly approaching the required sensitivity. This project is designed to make important contributions to gravitational-wave astronomy at a crucial time.Read moreRead less
Ancient stars: the origin of elements. The story of the origin of the elements fascinates mankind and touches many branches of science. This project combines new stellar population models of the oldest stars with new data from the Australian million-star GALactic Archaeology with HERMES (GALAH) survey to address basic astrophysical problems: mixing in stars, mass transfer in binary stars and measurement of the masses of the first stars. Knowing how these ancient stars behave is crucial to unders ....Ancient stars: the origin of elements. The story of the origin of the elements fascinates mankind and touches many branches of science. This project combines new stellar population models of the oldest stars with new data from the Australian million-star GALactic Archaeology with HERMES (GALAH) survey to address basic astrophysical problems: mixing in stars, mass transfer in binary stars and measurement of the masses of the first stars. Knowing how these ancient stars behave is crucial to understanding element production in the early Universe, both in our Milky Way and distant galaxies. By statistically comparing new models to the GALAH data, this project aims to measure the masses of the oldest galactic stars directly impacting branches of astrophysics from planets to galaxies.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?