Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in bi ....Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in biomedical image analysis. This project will lead to fundamental contributions as well as techniques that address both problems: extraction of relevant features information from multisubject brain imaging data sets of the same modality or from fusion of brain imaging data sets collected from multimodalities.Read moreRead less
Information access through web-scale question-answer pair finding, ranking and matching. This project will aim to take web search to a new level of sophistication in accepting queries in the form of complex natural language questions, and returning a ranked list of natural language answers automatically extracted from a broad range of web user forums.
Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge. This project aims to develop an automatic graph learning system for complex graph data analysis. Machine learning for graph data commonly requires significant human knowledge from both domain professionals as well as algorithm experts, rendering existing systems ineffective and unexplainable. This project expects to design novel graph learning techniques which automatically infer graph relations, learn graph models, adapts ....Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge. This project aims to develop an automatic graph learning system for complex graph data analysis. Machine learning for graph data commonly requires significant human knowledge from both domain professionals as well as algorithm experts, rendering existing systems ineffective and unexplainable. This project expects to design novel graph learning techniques which automatically infer graph relations, learn graph models, adapts existing knowledge to new domains, and provide explanations to the graph learning system. The research results should provide benefit to governments and businesses in many critical applications, such as bioassay activity prediction, credit assessment, and drug discovery and vaccine development in response to the pandemic.Read moreRead less
Exploiting Context in Multilingual Understanding and Generation. Automatic translation technologies produce incoherent and incorrect outputs in critical areas, such as health, finance, and law. This is due to translating sentences independently, without regard to the global extra-sentential context and rich linguistic structures inherent in the wider document context. This project aims to exploit global linguistic structures, capitalising on recent advances in deep neural networks, in order to g ....Exploiting Context in Multilingual Understanding and Generation. Automatic translation technologies produce incoherent and incorrect outputs in critical areas, such as health, finance, and law. This is due to translating sentences independently, without regard to the global extra-sentential context and rich linguistic structures inherent in the wider document context. This project aims to exploit global linguistic structures, capitalising on recent advances in deep neural networks, in order to generate coherent and faithful text. Expected outcome include next-generation computational technologies for language understanding and generation. This should significantly benefit document-based language technologies and increase their applications in a range of cultural, industrial, and health settings.Read moreRead less
Adaptive Context-Dependent Machine Translation for Heterogeneous Text. While automatic machine translation technologies are undoubtedly useful to a wide range of users, they often produce incoherent outputs for many types of input, for example, medical, literature, or even conversational text. This project will develop new adaptive machine translation systems to handle many domains and text styles, including heterogeneous mixed-domain inputs. It will develop multi-task machine learning methods f ....Adaptive Context-Dependent Machine Translation for Heterogeneous Text. While automatic machine translation technologies are undoubtedly useful to a wide range of users, they often produce incoherent outputs for many types of input, for example, medical, literature, or even conversational text. This project will develop new adaptive machine translation systems to handle many domains and text styles, including heterogeneous mixed-domain inputs. It will develop multi-task machine learning methods for training collections of domain-specific translation systems while leveraging correlations between domains. This approach will reduce the big data requirements of current translation systems, and improve translation quality across a wide range of different language pairs and application domains.Read moreRead less
Modelling, Identification and Control of Complex Networks. Australia has been well known for its leading research in systems and control and many real-world applications in, for instance, telecommunications, defence, power grids and life sciences. This project will further promote Australia's leading position in the emerging new research field - complex networks by theoretical breakthrough in modelling, identification and control of complex networks, and cutting-edge platform technology that can ....Modelling, Identification and Control of Complex Networks. Australia has been well known for its leading research in systems and control and many real-world applications in, for instance, telecommunications, defence, power grids and life sciences. This project will further promote Australia's leading position in the emerging new research field - complex networks by theoretical breakthrough in modelling, identification and control of complex networks, and cutting-edge platform technology that can help Australian energy industry to reduce greenhouse emissions. It will also result in education of the next generation research leaders in this emerging field.Read moreRead less
Ambient spatial intelligence: Spatial analysis and event detection in environmental geosensor networks. This project will design and test innovative new decentralised algorithms for responding to spatiotemporal queries in environmental monitoring networks. The research is essential for constructing larger, denser, and more reliable networks, helping to embed spatial intelligence within the environment itself (ambient spatial intelligence). The project builds on Australia's existing research exce ....Ambient spatial intelligence: Spatial analysis and event detection in environmental geosensor networks. This project will design and test innovative new decentralised algorithms for responding to spatiotemporal queries in environmental monitoring networks. The research is essential for constructing larger, denser, and more reliable networks, helping to embed spatial intelligence within the environment itself (ambient spatial intelligence). The project builds on Australia's existing research excellence in geographic information science. By making smarter use of spatial information, the project will further strengthen Australia's world-leading spatial information industry, and support sustainable and economic environmental management through important applications like conservation contracts and carbon sequestration monitoring.Read moreRead less
Ultrahigh-speed optical transport for sustaining the internet growth. Our society has entered an information era centred around the Internet. This project aims to study novel transport technologies to construct optical backbone networks supporting the Internet traffic. The project will keep Australia at the leading edge of exciting Terabit technologies as well as create commercial opportunities in Australia.
Parameter estimation for genetic time-series data: Theory and methods. This project aims to develop a novel computational framework for solving parameter estimation problems in evolutionary modelling by leveraging genetic time-series data measured by Next-Generation Sequencing technologies. It will foster international collaboration, cutting across disciplines. By introducing new techniques from signal processing and tools from random matrix theory commonly employed for mobile wireless communica ....Parameter estimation for genetic time-series data: Theory and methods. This project aims to develop a novel computational framework for solving parameter estimation problems in evolutionary modelling by leveraging genetic time-series data measured by Next-Generation Sequencing technologies. It will foster international collaboration, cutting across disciplines. By introducing new techniques from signal processing and tools from random matrix theory commonly employed for mobile wireless communications, it seeks to design scalable inference methods for resolving mutational fitness effects from genetic time-series measurements of complex evolving populations. This would enable new understanding of complex adaptive systems, such as pathogen evolution, host-immune dynamics, and acquisition of drug resistance. Read moreRead less
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.