Discovery Early Career Researcher Award - Grant ID: DE120101161
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Compressive sensing based probabilistic graphical models (PGM). The aim of the project is to develop fast, large scale probabilistic graphical models (PGM) learning and inference methods. The resulting system will be able to process large scale PGMs on a standard PC, and will be easily extendable to computer clustering for larger scale PGMs requiring higher precision.
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.
Advanced analytics utilising conjoint mining of data and content with applications in business, bio-medicine and electrical power systems. This project will provide techniques that enable effective analysis of unstructured content and related information from relational databases in a conjoint manner. These techniques will be applied in the business, bio-medicine and electrical power systems domains.
Studying privacy protection methods for multiple independent data releases. Privacy is at risk if two or more published data sets contain overlapping individuals even when each data set is anonymised. This project will investigate if existing anonymisation methods can handle this privacy risk, and will study new solutions. The outcomes will potentially have a great impact on data anonymisation research and applications.
Theoretical Foundations of Ethical Machine Learning. The project aims to develop a systematic theory of ethical machine learning. Machine learning is a powerful and pervasive technology that is already having a huge impact on Australia. When applied to data about people there are a range of ethical harms that can arise (fairness, and privacy are two of them). The project aims to develop a rigorously grounded foundation for managing such ethical harms. For example it will allow the quantification ....Theoretical Foundations of Ethical Machine Learning. The project aims to develop a systematic theory of ethical machine learning. Machine learning is a powerful and pervasive technology that is already having a huge impact on Australia. When applied to data about people there are a range of ethical harms that can arise (fairness, and privacy are two of them). The project aims to develop a rigorously grounded foundation for managing such ethical harms. For example it will allow the quantification of the inevitable trade-offs between fairness and utility. The benefits of the project should include better ways of managing these trade-offs, a competitive advantage for Australian firms developing the technology, and will ensure that the country retains a social license to use the technology.Read moreRead less
The next generation speaker recognition system. The next generation of speaker recognition technologies developed through this project will enable secure person authentication by voice in financial transactions and benefit the community through the elimination of identity fraud. This project will safeguard Australia by identifying criminal suspects using their voice and combat terrorism by using voice to locate and track terrorists.
Hybrid optimisation for automatic large-scale video annotation. Optimization is the basis for solving many problems in Computer Vision, such as three-dimensional geometry recovery, image segmentation, scene labeling and object recognition. This project will develop new optimisation techniques and demonstrate their suitability for large-scale video annotation, which is key to visual data mining and scene understanding.
Discovery Early Career Researcher Award - Grant ID: DE130101775
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Distributed large-scale optimisation methods in computer vision. With the number of images and video available over the internet reaching billions and growing, the need for new tools for handling and interpreting such huge amounts of data is quickly becoming apparent. This project will focus on developing new optimisation methods for efficiently computing solutions for a broad class of large-scale problems.
Australian Laureate Fellowships - Grant ID: FL200100176
Funder
Australian Research Council
Funding Amount
$3,128,080.00
Summary
Theoretical Foundations of Ethical Machine Learning. The project will develop a systematic theory of ethical machine learning. Machine learning is a powerful and pervasive technology that is already having a huge impact on Australia. When applied to data about people there are a range of ethical harms that can arise (fairness, and privacy are two of them). The project will develop a rigorously grounded foundation for managing such ethical harms. For example it will allow the quantification of t ....Theoretical Foundations of Ethical Machine Learning. The project will develop a systematic theory of ethical machine learning. Machine learning is a powerful and pervasive technology that is already having a huge impact on Australia. When applied to data about people there are a range of ethical harms that can arise (fairness, and privacy are two of them). The project will develop a rigorously grounded foundation for managing such ethical harms. For example it will allow the quantification of the inevitable trade-offs between fairness and utility. The benefits of the project will include the best possible ways of managing these trade-offs, competitive advantage for Australian firms developing the technology, and will ensure that the country retains a social license to use the technology.Read moreRead less