Classification methods for providing personalised and class decisions. This project provides a novel approach to the clustering of multivariate samples on entities in a class that automatically matches the sample clusters across the entities, allowing for inter-sample variation between the samples in a class. The project aims to develop a widely applicable, mixture-model-based framework for the simultaneous clustering of multivariate samples with inter-sample variation in a class and for the mat ....Classification methods for providing personalised and class decisions. This project provides a novel approach to the clustering of multivariate samples on entities in a class that automatically matches the sample clusters across the entities, allowing for inter-sample variation between the samples in a class. The project aims to develop a widely applicable, mixture-model-based framework for the simultaneous clustering of multivariate samples with inter-sample variation in a class and for the matching of the clusters across the entities in the class. The project will use a statistical approach to automatically match the clusters, since the overall mixture model provides a template for the class. It will provide a basis for discriminating between different classes in addition to the identification of atypical data points within a sample and of anomalous samples within a class. Key applications include biological image analysis and the analysis of data in flow cytometry which is one of the fundamental research tools for the life scientist.Read moreRead less
Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently ....Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently learn complex interaction and navigation behaviours that transfer to unseen environments. Our research should benefit new applications in domains of economic and societal importance that are currently too complex, unsafe, and uncertain for robot assistants, such as aged care, advanced manufacturing and domestic robotics.Read moreRead less
Parent involvement goes online: New ecologies of school-home relations. This project aims to: identify forms of digital exclusion and inclusion impacting on parents’ ability to support their children’s education; produce a new conceptual model of technologically mediated school-home relationships; and provide a comprehensive map of school-home connected digital tools and services. Combining a detailed survey of 500 school leaders with innovative networked case studies across three schools and 18 ....Parent involvement goes online: New ecologies of school-home relations. This project aims to: identify forms of digital exclusion and inclusion impacting on parents’ ability to support their children’s education; produce a new conceptual model of technologically mediated school-home relationships; and provide a comprehensive map of school-home connected digital tools and services. Combining a detailed survey of 500 school leaders with innovative networked case studies across three schools and 18 families, this will be the first national study to comprehensively describe and analyse home-school partnerships in the digital age. It will provide policy and educational leadership with a roadmap for addressing barriers to digital inclusion, as schools advance their integration of digital platforms. Read moreRead less
Data analytics-based tools and methods to enhance self-regulated learning. This project aims to develop student self-regulated learning skills by harnessing the potential of Big Data analytics. The project expects to generate new knowledge at the intersection of learning analytics, educational technology, learning sciences and teaching practice resulting from novel data collection and analysis tools and methods. The outputs are expected to include insights into metacognitive, motivational, and t ....Data analytics-based tools and methods to enhance self-regulated learning. This project aims to develop student self-regulated learning skills by harnessing the potential of Big Data analytics. The project expects to generate new knowledge at the intersection of learning analytics, educational technology, learning sciences and teaching practice resulting from novel data collection and analysis tools and methods. The outputs are expected to include insights into metacognitive, motivational, and technical issues facing analytics-based personalised feedback. The outcomes are intended to offer benefits for developing pedagogical and the design of educational technology. The outcomes can result in improved student learning outcomes in higher education to ensure graduates are prepared for the digital economy.Read moreRead less
Walking with dinosaurs in the Kimberley: mapping the Cretaceous landscapes of the Dampier Peninsula. The coastline of the Dampier Peninsula, Western Australia, preserves what is arguably one the largest and most significant stretches of dinosaur track-sites in the world. Despite recent National Heritage listing, the majority of these tracksites are largely undocumented, such that their full scientific significance is poorly understood. The aim of this project is to digitally map the dinosaur tra ....Walking with dinosaurs in the Kimberley: mapping the Cretaceous landscapes of the Dampier Peninsula. The coastline of the Dampier Peninsula, Western Australia, preserves what is arguably one the largest and most significant stretches of dinosaur track-sites in the world. Despite recent National Heritage listing, the majority of these tracksites are largely undocumented, such that their full scientific significance is poorly understood. The aim of this project is to digitally map the dinosaur tracksites of the Dampier Peninsula, utilising high-resolution aerial photography with both manned and unmanned aircraft, airborne and hand-held LiDAR imaging, and digital photogrammetry. The results will allow us to construct high-resolution, three-dimensional digital outcrop models of the tracksites, and bring the 130 million-year-old landscapes back to life.Read moreRead less
Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features t ....Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features to analyse in each modality and the hidden relationships between them. The use of deep belief networks has produced promising results in several fields, such as speech recognition, and so this project believes that our approach has the potential to improve both the sensitivity and specificity of breast cancer detection.Read moreRead less
Towards knowledge discovery from imperfect and evolving data. Information extraction from data is critical, both to analyse and protect consumer data. However, many learning techniques are developed using perfect, static datasets, quite different to messy, ever-changing real-world data. This project aims to develop data analytics techniques that can extract accurate information in complex structures from imperfect/incomplete data that changes over time. Expected outcomes are a prototype tool, te ....Towards knowledge discovery from imperfect and evolving data. Information extraction from data is critical, both to analyse and protect consumer data. However, many learning techniques are developed using perfect, static datasets, quite different to messy, ever-changing real-world data. This project aims to develop data analytics techniques that can extract accurate information in complex structures from imperfect/incomplete data that changes over time. Expected outcomes are a prototype tool, tested on real datasets, that combines new techniques in data modelling, algorithm development, and system design. Likely benefits are enhanced Australia's competence in data science through student training and new, robust data tools relevant to critical sectors such as cybersecurity, healthcare, and defence.Read moreRead less
Redesigning Landcare policy to better coordinate across landholders. This project aims to study how landscape-sensitive economic incentives and social norms can be leveraged to enhance the short- and long-term effectiveness of conservation programs. It will yield new knowledge for innovative designs in conservation contracting that is urgently needed to address worsening environmental threats in Australia and worldwide. In collaboration with Nobel laureate Vernon Smith’s team, new methods and pr ....Redesigning Landcare policy to better coordinate across landholders. This project aims to study how landscape-sensitive economic incentives and social norms can be leveraged to enhance the short- and long-term effectiveness of conservation programs. It will yield new knowledge for innovative designs in conservation contracting that is urgently needed to address worsening environmental threats in Australia and worldwide. In collaboration with Nobel laureate Vernon Smith’s team, new methods and protocols will improve our ability to generate better data and better understand how social and incentive mechanisms can constructively interact to facilitate collaborative environmental action. Results will help make the achievement of environmental targets and the use of public funds more cost-effective. Read moreRead less
Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the ....Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the first approach capable of discovering previously unknown biomarkers associated with important clinical outcomes. The project will validate the approach on a real-world case study data set concerning the prediction of five-year survival of chronic disease.Read moreRead less
The fundamental equations for inversion of operator pencils. This project seeks to deepen understanding of how complex systems may be significantly changed by incremental changes to ambient conditions. Mathematical models of complex systems (climate change processes, optimal driving strategies, efficient distribution policies, effective search routines) often depend on key parameters. If small perturbations to the parameters cause large changes to the solution, then the perturbations are said to ....The fundamental equations for inversion of operator pencils. This project seeks to deepen understanding of how complex systems may be significantly changed by incremental changes to ambient conditions. Mathematical models of complex systems (climate change processes, optimal driving strategies, efficient distribution policies, effective search routines) often depend on key parameters. If small perturbations to the parameters cause large changes to the solution, then the perturbations are said to be singular. This project aims to reveal the underlying mathematical structures and develop new computational algorithms to analyse a general class of perturbed systems both locally near an isolated singularity and globally. It plans to use these algorithms to solve systems of equations, calculate generalised inverse operators, examine perturbed Markov processes, and estimate exit times from meta-stable states in stochastic population dynamics.Read moreRead less