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
Quantum Generative Diffusion Models for Molecular Research. This project will devise quantum generative diffusion models to equip classical counterparts with the ability to harness quantum data that naturally arise in molecular research. Theoretical foundations for analysing fast sampling methods with the help of inductive bias regarding the input data and employed circuits will validate efficient quantum generative diffusion models that have training and sampling advantages over classical count ....Quantum Generative Diffusion Models for Molecular Research. This project will devise quantum generative diffusion models to equip classical counterparts with the ability to harness quantum data that naturally arise in molecular research. Theoretical foundations for analysing fast sampling methods with the help of inductive bias regarding the input data and employed circuits will validate efficient quantum generative diffusion models that have training and sampling advantages over classical counterparts. Outcomes include applications in molecular conformation generation, compound screening, and drug design. The innovative research will significantly benefit Australia’s science, industry and health, and will maintain Australia’s global leading role in quantum machine learning and molecular research.Read moreRead less
Optimising students’ academic trajectories: The role of growth (‘personal best’) goals. Too many students fail to reach their academic potential and, as a result, they risk being systematically denied a sense of academic ‘success’ and progress. Through a focus on academic growth (and ‘personal bests’), this research project traverses complex terrain to identify the role of growth goals and growth goal setting in students’ academic trajectories. It also tackles methodological challenges that have ....Optimising students’ academic trajectories: The role of growth (‘personal best’) goals. Too many students fail to reach their academic potential and, as a result, they risk being systematically denied a sense of academic ‘success’ and progress. Through a focus on academic growth (and ‘personal bests’), this research project traverses complex terrain to identify the role of growth goals and growth goal setting in students’ academic trajectories. It also tackles methodological challenges that have impeded research progress in this compelling area. Through strategic international and institutional links, the research program will identify innovative approaches to academic growth and growth goals that will significantly assist pedagogy and psychology aimed at optimising students’ academic potential.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
Solving the inert knowledge problem. A central goal of education is for students to transfer what they learn to new contexts or problems. Indeed, expert reasoning is often characterised by seeing the deep structural commonalities across seemingly disparate situations. However, the knowledge students acquire is notoriously inert, tied to the specifics of the learning examples. This project aims to move towards solving 'the inert knowledge problem' by investigating how humans learn concepts define ....Solving the inert knowledge problem. A central goal of education is for students to transfer what they learn to new contexts or problems. Indeed, expert reasoning is often characterised by seeing the deep structural commonalities across seemingly disparate situations. However, the knowledge students acquire is notoriously inert, tied to the specifics of the learning examples. This project aims to move towards solving 'the inert knowledge problem' by investigating how humans learn concepts defined by abstract relational structure, and by designing educational applications that enhance the use of relational learning mechanisms in students with a wide range of cognitive abilities.Read moreRead less
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
Transforming primary teachers' representational practices: effects on students' scientific reasoning and discourse within contemporary sciences. Training teachers to appropriately represent and communicate scientific information is critically important for promoting scientific thinking and learning in students. This research is critical to securing Australia's future interests in developing new and emerging frontier science and technologies through the engagement and retention of students.
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
Discovery Early Career Researcher Award - Grant ID: DE150100731
Funder
Australian Research Council
Funding Amount
$361,744.00
Summary
Contextual supports for the early development of self-regulated learning. How do young children develop critical learning behaviours that are the key for their future academic success? What kinds of environments support this development? This project aims to answer these questions by investigating the development of regulatory behaviours (with a specific focus on self-regulated learning) during the first two years of schooling, and identifying critical contextual variables at home and at school ....Contextual supports for the early development of self-regulated learning. How do young children develop critical learning behaviours that are the key for their future academic success? What kinds of environments support this development? This project aims to answer these questions by investigating the development of regulatory behaviours (with a specific focus on self-regulated learning) during the first two years of schooling, and identifying critical contextual variables at home and at school impacting on this development. Findings from this research will provide crucial information for the design of family and practitioner-based interventions helping to improve the educational outcomes of young Australians.Read moreRead less