Modelling Adversarial Noise for Trustworthy Data Analytics. Adversarial robustness is a core property of trustworthy machine learning. This project aims to equip machines with the ability to model adversarial noise for defending adversarial attacks. The project expects to produce the next great step for artificial intelligence – the potential to robustly explore and exploit deceptive data. Expected outcomes of this project include theoretical foundations for modelling adversarial noise and the n ....Modelling Adversarial Noise for Trustworthy Data Analytics. Adversarial robustness is a core property of trustworthy machine learning. This project aims to equip machines with the ability to model adversarial noise for defending adversarial attacks. The project expects to produce the next great step for artificial intelligence – the potential to robustly explore and exploit deceptive data. Expected outcomes of this project include theoretical foundations for modelling adversarial noise and the next generation of intelligent systems to accommodate data in a noisy and hostile environment. This should benefit science, society, and the economy nationally and internationally through the applications to trustworthily analyse their corresponding complex data. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230100495
Funder
Australian Research Council
Funding Amount
$422,154.00
Summary
Structured Federated Learning for Personalised Intelligence on Devices. The project aims to develop a new structured federated machine-learning framework to enhance the customisation of artificial intelligence across mobile and smart devices. It seeks to enable users to receive customised services on their devices without sending their sensitive personal data to a cloud service provider. Anticipated benefits include greater privacy, data security and device performance, as well as better end-use ....Structured Federated Learning for Personalised Intelligence on Devices. The project aims to develop a new structured federated machine-learning framework to enhance the customisation of artificial intelligence across mobile and smart devices. It seeks to enable users to receive customised services on their devices without sending their sensitive personal data to a cloud service provider. Anticipated benefits include greater privacy, data security and device performance, as well as better end-user experience. Expected outcomes of this research include new knowledge, toolkits and algorithms for use in developing machine-learning based secure, efficient and fault-tolerant technologies for software applications, mobile services, cloud computing, autonomous vehicles and advanced manufacturing processes.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220100052
Funder
Australian Research Council
Funding Amount
$437,020.00
Summary
Impacts of the apartment boom on public transport in Australian cities. This project aims to investigate the impacts of high density housing on public transport use and service provision to directly inform policy and practice. Recent growth in high density housing along public transport corridors is associated with overcrowded public transport services in Australian cities, yet this complex and interconnected relationship is not well understood. This project expects to generate new knowledge in ....Impacts of the apartment boom on public transport in Australian cities. This project aims to investigate the impacts of high density housing on public transport use and service provision to directly inform policy and practice. Recent growth in high density housing along public transport corridors is associated with overcrowded public transport services in Australian cities, yet this complex and interconnected relationship is not well understood. This project expects to generate new knowledge in the field of transport and land use integration and produce much needed cross-sectional and longitudinal evidence of the impacts of the apartment boom on public transport. Anticipated benefits include reduced overcrowding on public transport, improved travel choices and enhanced liveability in Australian cities.Read moreRead less
A Road Out of Motion Sickness in Autonomous Vehicles. Autonomous vehicles have found to provide significant improvements in safety and efficiency, as well as the potential to comfortably engage in other activities including work and entertainment. Motion sickness is particularly a significant source of concern in this regard, with factors ranging from demographics, vehicle kinematics to in-vehicle designs affecting the likelihood of discomfort. This study aims to (1) understanding factors induci ....A Road Out of Motion Sickness in Autonomous Vehicles. Autonomous vehicles have found to provide significant improvements in safety and efficiency, as well as the potential to comfortably engage in other activities including work and entertainment. Motion sickness is particularly a significant source of concern in this regard, with factors ranging from demographics, vehicle kinematics to in-vehicle designs affecting the likelihood of discomfort. This study aims to (1) understanding factors inducing motion sickness in AVs (2) Evaluating individuals’ preferences between comfort and travel attributes (including in-vehicle tasks) (3) Develop and evaluate mitigation strategies for motion sickness in AVs. Insights from this research will help improve adoption of automated vehicles on the roadways.
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Excellent researchers: Using learner profiles to enhance research learning. Recent evidence concerning metacognitive learning and affect reveals that research degree candidates are a diverse group of learners, and little is known about explaining wasteful attrition, stress and delays in progress. Such a study is essential, especially given the growth in research degrees, new transitional pathways, diversity in candidate backgrounds and chronic high attrition. This longitudinal study applies new ....Excellent researchers: Using learner profiles to enhance research learning. Recent evidence concerning metacognitive learning and affect reveals that research degree candidates are a diverse group of learners, and little is known about explaining wasteful attrition, stress and delays in progress. Such a study is essential, especially given the growth in research degrees, new transitional pathways, diversity in candidate backgrounds and chronic high attrition. This longitudinal study applies new findings about doctoral learning profiles in a direct intervention (DOCLearnPro) that targets individual differences across students in doctoral and master’s degrees to improve learning outcomes significantly and contribute theoretically, methodologically and substantively in order to advance understanding of researcher development.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240101089
Funder
Australian Research Council
Funding Amount
$436,847.00
Summary
Trustworthy Hypothesis Transfer Learning. It is urgent to develop a new hypothesis transfer learning scheme that can overcome potential risks when finetuning unreliable large-scale pre-trained models. This project aims to develop an advanced and reliable scheme of hypothesis transfer learning, called Trustworthy Hypothesis Transfer Learning (TrustHTL). A new theoretically guaranteed heterogeneous hypothesis transfer learning framework will be developed to handle heterogeneous situations; a metho ....Trustworthy Hypothesis Transfer Learning. It is urgent to develop a new hypothesis transfer learning scheme that can overcome potential risks when finetuning unreliable large-scale pre-trained models. This project aims to develop an advanced and reliable scheme of hypothesis transfer learning, called Trustworthy Hypothesis Transfer Learning (TrustHTL). A new theoretically guaranteed heterogeneous hypothesis transfer learning framework will be developed to handle heterogeneous situations; a methodology to disinherit risks of pre-trained models and a new fuzzy relation based distributional discrepancy in heterogeneous transfer learning scenarios. The outcomes should significantly improve the reliability of machine learning with benefits for safety learning in data analytics.Read moreRead less
Toward Human-guided Safe Reinforcement Learning in the Real World. This project aims to investigate human-guided safe reinforcement learning (RL). Safe RL is an important topic that could enable real applications of RL systems by addressing safety constraints. Existing safe RL assumes the availability of specified safety constraints in mathematical or logical forms. This project proposes to study learning safety objectives from information provided directly by humans or indirectly via language m ....Toward Human-guided Safe Reinforcement Learning in the Real World. This project aims to investigate human-guided safe reinforcement learning (RL). Safe RL is an important topic that could enable real applications of RL systems by addressing safety constraints. Existing safe RL assumes the availability of specified safety constraints in mathematical or logical forms. This project proposes to study learning safety objectives from information provided directly by humans or indirectly via language models, and human-guided continuous correction for safety improvements. The established theories and developed algorithms will advance frontier technologies in AI and contribute to a wide range of real applications of safe RL, such as robotics and autonomous driving, bringing enormous social and economic benefits. Read moreRead less
Quantifying Ethics-related Metrics for Transport Network Systems. This project aims to identify ethics-related metrics for improving the design of transport network services, and augment the social benefits of transport systems to relevant user groups. This project is anticipated to conceive, implement and validate new methodologies to solve challenging optimisation problems aiming at promoting ethics in transport systems via the provision of incentives to transport services providers. The outco ....Quantifying Ethics-related Metrics for Transport Network Systems. This project aims to identify ethics-related metrics for improving the design of transport network services, and augment the social benefits of transport systems to relevant user groups. This project is anticipated to conceive, implement and validate new methodologies to solve challenging optimisation problems aiming at promoting ethics in transport systems via the provision of incentives to transport services providers. The outcomes of this project are expected to support the emergence of ethical transport systems and to address fundamental societal and economical challenges induced by utility-driven transport services. This project will help in positioning Australia as a global leader in the field of ethical transport network systems.Read moreRead less
Personalised public transport. This project aims to address urban congestion by utilising people’s travel plans to coordinate journeys. The project expects to generate new knowledge in scalable optimisation, based on innovative modelling of urban transport, and tested on historical data from Melbourne. The expected outcomes of the project are an active transport database and optimised mode choice and routing system, with predicted reductions in congestion based on simulation of its use. This pro ....Personalised public transport. This project aims to address urban congestion by utilising people’s travel plans to coordinate journeys. The project expects to generate new knowledge in scalable optimisation, based on innovative modelling of urban transport, and tested on historical data from Melbourne. The expected outcomes of the project are an active transport database and optimised mode choice and routing system, with predicted reductions in congestion based on simulation of its use. This project aims to design an urban trip advisory system that could be followed by automated vehicles as well as human drivers, to reduce the financial and environmental cost of current urban congestion.Read moreRead less
Walking the city: Digital infrastructure for pedestrian mobility. Pedestrian access, flow and management are critical for urban life. However, compared to other forms of mobility pedestrian mobility is significantly more complex. Currently, various incompatible pedestrian route graphs in both outdoor and indoor environments render any analysis biased and non-transparent. This project aims to solve this problem by developing a universal and necessarily hierarchical pedestrian route graph to suppo ....Walking the city: Digital infrastructure for pedestrian mobility. Pedestrian access, flow and management are critical for urban life. However, compared to other forms of mobility pedestrian mobility is significantly more complex. Currently, various incompatible pedestrian route graphs in both outdoor and indoor environments render any analysis biased and non-transparent. This project aims to solve this problem by developing a universal and necessarily hierarchical pedestrian route graph to support critical applications such as urban walkability (health), space and asset management (guidance, flow management), and public safety (evacuation). In contrast to conventional algorithms, we will take a novel approach based on human cognition to define this universal graph and then integrate topology and geometry.Read moreRead less