Automated assessment of data quality in biological knowledge resources. This project aims to develop methods for identifying poor quality data in biological databases. Research in biomedicine is underpinned by massive databases of biological data. Data quality is largely managed through manual curation, but automated methods to assess quality are critically needed. This project expects to develop a suite of computational tools for assessing biological data quality, utilising an innovative approa ....Automated assessment of data quality in biological knowledge resources. This project aims to develop methods for identifying poor quality data in biological databases. Research in biomedicine is underpinned by massive databases of biological data. Data quality is largely managed through manual curation, but automated methods to assess quality are critically needed. This project expects to develop a suite of computational tools for assessing biological data quality, utilising an innovative approach based on network analysis of database record connectivity. These tools will enable quantifying data quality at scale. Researchers, evidence-based decision-makers in biomedicine, and the analytical or predictive tools that use this data will make more reliable inferences and decisions.Read moreRead less
Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it i ....Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it is not clear to the end user how reliable the results are. The outcomes intend to deliver advanced knowledge and capability in artificial intelligence and machine learning that Australia urgently needs to capitalise on bringing deep learning into practical applications delivering economic, commercial and social impact.Read moreRead less
An intelligent machine modelling assistant for combinatorial optimisation. This project aims to discover key fundamental technologies for automating assistance to non-expert users in the formulation of mathematical models. Through automating the modelling of combinatorial optimization problems, this research will generate new knowledge to address the fundamental challenges of automatic mathematical modelling. This intelligent assistant will enable synthesis of new mathematical models through th ....An intelligent machine modelling assistant for combinatorial optimisation. This project aims to discover key fundamental technologies for automating assistance to non-expert users in the formulation of mathematical models. Through automating the modelling of combinatorial optimization problems, this research will generate new knowledge to address the fundamental challenges of automatic mathematical modelling. This intelligent assistant will enable synthesis of new mathematical models through the utilisation of pioneering natural language processing components and novel custom-made machine-readable knowledge bases. The outcome of this research will broaden access to high-quality models by non-expert workforce and alleviate the shortage of expert mathematicians, bringing significant social and economic benefits.Read moreRead less
Large-scale computational modelling of epidemics in Australia. The project aims to develop novel computational epidemiological models to contribute to guidelines for optimal prophylaxis, vaccination and case management. Emerging threats posed by infectious diseases and bioterrorism could have dramatic effects on the Australian population, productivity and economy. The project aims to improve the accuracy and scope of modern computational epidemiological models by integrating large-scale Census d ....Large-scale computational modelling of epidemics in Australia. The project aims to develop novel computational epidemiological models to contribute to guidelines for optimal prophylaxis, vaccination and case management. Emerging threats posed by infectious diseases and bioterrorism could have dramatic effects on the Australian population, productivity and economy. The project aims to improve the accuracy and scope of modern computational epidemiological models by integrating large-scale Census datasets and explicitly simulating the entire population down to the level of single individuals, coupled with complex network-based and information flow analysis. The intended outcomes include a more precise and efficient forecasting of critical epidemic dynamics, and increased effectiveness of prevention, mitigation and management of socio-economic, socio-ecological and national security crises.Read moreRead less
Modelling and simulation of self-organised behaviour in biological and bio-inspired systems. Understanding self-organised systems is fundamental in biology and bio-inspired engineering. The project develops sophisticated mathematical modelling techniques and high performance simulation methods for such systems. This will increase our capacity to explain complex biological behaviour and to produce reliable bio-inspired engineering solutions
Modelling collective behaviour to protect social insect ecosystem services. This project aims to use mathematical models and computer simulations and biological experiments to investigate how social insects adapt to environmental stress, for example due to climate change and pollution. Fundamental to the adaptability of social insects are the complex mechanisms that allow colonies to maintain a carefully balanced division of labour (DOL). This project builds on evolutionary game theory to develo ....Modelling collective behaviour to protect social insect ecosystem services. This project aims to use mathematical models and computer simulations and biological experiments to investigate how social insects adapt to environmental stress, for example due to climate change and pollution. Fundamental to the adaptability of social insects are the complex mechanisms that allow colonies to maintain a carefully balanced division of labour (DOL). This project builds on evolutionary game theory to develop a new approach for analysing how environmental factors impact on DOL and thus colony viability. The project will deliver new methods to assess and predict the impact of environmental stress This will ultimately help to protect these keystones of biodiversity and the significant ecosystem services they provide as pest-control agents, through pollination, seed dispersal, and soil conditioning.Read moreRead less
Data-driven Traffic Analytics for Incident Analysis and Management. Traffic incidents are among the primary concerns of all transport authorities around the world due to their significant impact in terms of traffic congestion and delay, air and noise pollution, and management cost. This project aims to address incident analysis and management in complex and multi-modal traffic networks by combining multidisciplinary research efforts from transportation engineering and data science. The intended ....Data-driven Traffic Analytics for Incident Analysis and Management. Traffic incidents are among the primary concerns of all transport authorities around the world due to their significant impact in terms of traffic congestion and delay, air and noise pollution, and management cost. This project aims to address incident analysis and management in complex and multi-modal traffic networks by combining multidisciplinary research efforts from transportation engineering and data science. The intended outcomes will be an innovative incident analysis and management framework synergising traffic data analytics and traffic simulation modelling as well as its key enabling techniques and prototype systems. This will significantly help mitigate incident impacts on daily commuters.Read moreRead less
Enhancing and supporting deliberations within multidisciplinary decision teams. A group’s knowledge and reasoning processes are rarely conducted or recorded systematically; a problem particularly pressing for medical decisions made by multidisciplinary teams. This project will perform the first integration of knowledge management principles with group reasoning process models to create an online environment for enhancing the quality of group decisions and their documentation. The online environm ....Enhancing and supporting deliberations within multidisciplinary decision teams. A group’s knowledge and reasoning processes are rarely conducted or recorded systematically; a problem particularly pressing for medical decisions made by multidisciplinary teams. This project will perform the first integration of knowledge management principles with group reasoning process models to create an online environment for enhancing the quality of group decisions and their documentation. The online environment will help medical teams use documented and non-documented knowledge when more than one medical condition is present and no formal guidelines are applicable. The project advances theories of knowledge in groups and is extendable to any deliberations on complex problems.Read moreRead less
Integrating Mobility on Demand in urban transport infrastructures. Australia’s major cities are substantially challenged for public transport services due to the dispersed and low population densities, and thus, roads are at or beyond their capacity. Smarter demand-responsive public transport services are therefore needed. This project studies the viability of such a service under a variety of scenarios.
Subject-specific computational models for accurate evaluation of muscle function in human locomotion. The purpose of this project is to advance current understanding of muscle function during human locomotion. The most significant outcome will be the development of novel computational tools that can play a pivotal role in the healthcare industry through the prevention, diagnosis and treatment of movement disorders.