Efficient and effective methods for classifying massive time series data. This project aims to transform the theory and practice of time series classification. The current state of the art cannot handle the massive numbers of time series that describe many critical problems facing humanity, such as disease transmission and climate change. This project seeks to develop methods that can analyse dynamic processes at global scale, delivering the most accurate classifiers feasible within a given comp ....Efficient and effective methods for classifying massive time series data. This project aims to transform the theory and practice of time series classification. The current state of the art cannot handle the massive numbers of time series that describe many critical problems facing humanity, such as disease transmission and climate change. This project seeks to develop methods that can analyse dynamic processes at global scale, delivering the most accurate classifiers feasible within a given computational budget. Expected outcomes of this project include efficient, effective and broadly applicable time series classification technologies. This should provide significant benefits to myriad sectors, transforming data science for time series problems and supporting innovation in industry, commerce and government.Read moreRead less
Causal Knowledge-Empowered Adaptive Federated Learning. Federated learning tools are a promising framework for collaborative machine learning (ML) that also maintain data privacy; however, their ability to model heterogeneous data remains a key challenge. This project aims to develop a new learning scheme for coordinated training of ML models that successfully bridges variable data distributions. The framework proposed will be the first globally that can use causal knowledge to 1) handle data he ....Causal Knowledge-Empowered Adaptive Federated Learning. Federated learning tools are a promising framework for collaborative machine learning (ML) that also maintain data privacy; however, their ability to model heterogeneous data remains a key challenge. This project aims to develop a new learning scheme for coordinated training of ML models that successfully bridges variable data distributions. The framework proposed will be the first globally that can use causal knowledge to 1) handle data heterogeneity across devices and 2) address the real-world challenges when only a subset of devices have labelled data. Expected outcomes and benefits include the theoretical underpinnings and algorithms of causality-based collaborative training of ML models while better preserving the users’ data privacy.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230100055
Funder
Australian Research Council
Funding Amount
$445,437.00
Summary
Illuminating the dark Universe with explosive astrophysical events. Explosive astrophysical events are critical to understand what the Universe is made of and its physics. This project aims to single out the most exciting exploding stars and extreme events out of the millions detected each night at the world’s largest optical telescope. It will magnify Australian leadership and optimise investment in astronomical facilities by obtaining unique information before these events fade forever. Expect ....Illuminating the dark Universe with explosive astrophysical events. Explosive astrophysical events are critical to understand what the Universe is made of and its physics. This project aims to single out the most exciting exploding stars and extreme events out of the millions detected each night at the world’s largest optical telescope. It will magnify Australian leadership and optimise investment in astronomical facilities by obtaining unique information before these events fade forever. Expected outcomes include improved knowledge on the nature of exploding stars and the discovery of new events and physical processes. It will benefit the Australian community at large by training young Australians in data-intensive technologies required to lead ground-breaking research and advance our innovative economy.Read moreRead less