Systems-based Study, Intervention, Diagnosis And Control Of Gastrointestinal Parasites
Funder
National Health and Medical Research Council
Funding Amount
$2,538,220.00
Summary
Gastrointestinal parasites cause billions of infections and hundreds of thousand of deaths globally each year. Even in developed countries, these parasites remain an important public health risk, through the cost of their control, the acute impacts of infection and their contribution to post-infectious irritable bowel syndrome and chronic fatigue. My team employs cutting-edge methods to improve their diagnosis, surveillance, treatment and control.
Data-led bioengineering to uncover hidden chemical wealth in bacteria. The soil bacteria Nocardia are an untapped source of industrially prized chemical compounds called natural products. This project aims to develop innovative bioprospecting genomics technologies built from the disciplines of microbiology, biochemistry and computational statistics to discover hundreds of new natural products in Nocardia. This project will unlock the diversity of potent new enzymes and molecules with high econom ....Data-led bioengineering to uncover hidden chemical wealth in bacteria. The soil bacteria Nocardia are an untapped source of industrially prized chemical compounds called natural products. This project aims to develop innovative bioprospecting genomics technologies built from the disciplines of microbiology, biochemistry and computational statistics to discover hundreds of new natural products in Nocardia. This project will unlock the diversity of potent new enzymes and molecules with high economic value that could include insecticides to protect crops, bioactives to fight diseases, or new enzymes for food and biofuel production. This research unlocks enormous hidden chemical potential in soil bacteria, to build sustainable national economic growth through innovative, high-value industrial chemical development.Read moreRead less
Privacy-Aware and Personalised Explanation Overlays for Recommender Systems. AI-powered recommender systems provide recommendations for daily lives, but they need to be legally interpretable and explainable. This project aims to transform existing black-box recommender models into transparent and trustworthy decision-support systems. The resulting tools will offer granular, explorable rationales for the recommendations in real time, creating greater public confidence while advancing the field. ....Privacy-Aware and Personalised Explanation Overlays for Recommender Systems. AI-powered recommender systems provide recommendations for daily lives, but they need to be legally interpretable and explainable. This project aims to transform existing black-box recommender models into transparent and trustworthy decision-support systems. The resulting tools will offer granular, explorable rationales for the recommendations in real time, creating greater public confidence while advancing the field. The expected outcomes include graph embedding methods for capturing real-world relationships in all their messiness and complexity. The anticipated contributions include impartial and accountable recommender models that are resistant to adversarial attacks and that slow the spread of misinformation.Read moreRead less
Efficient and effective methods for classifying massive time series data. This project aims to transform the theory and practice of time series classification. The current state of the art cannot handle the massive numbers of time series that describe many critical problems facing humanity, such as disease transmission and climate change. This project seeks to develop methods that can analyse dynamic processes at global scale, delivering the most accurate classifiers feasible within a given comp ....Efficient and effective methods for classifying massive time series data. This project aims to transform the theory and practice of time series classification. The current state of the art cannot handle the massive numbers of time series that describe many critical problems facing humanity, such as disease transmission and climate change. This project seeks to develop methods that can analyse dynamic processes at global scale, delivering the most accurate classifiers feasible within a given computational budget. Expected outcomes of this project include efficient, effective and broadly applicable time series classification technologies. This should provide significant benefits to myriad sectors, transforming data science for time series problems and supporting innovation in industry, commerce and government.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
Advanced Machine Learning with Bilevel Optimization. There is an urgent need to develop a new machine learning (ML) paradigm that can overcome data-privacy and model-size constraints in real-world applications. This project aims to develop an advanced paradigm of ML with bilevel optimisation, called bilevel ML. A theoretically-guaranteed fast approximate solver and a new fuzzy bilevel learning framework will be developed to achieve the aim in complex situations; a methodology to transfer knowled ....Advanced Machine Learning with Bilevel Optimization. There is an urgent need to develop a new machine learning (ML) paradigm that can overcome data-privacy and model-size constraints in real-world applications. This project aims to develop an advanced paradigm of ML with bilevel optimisation, called bilevel ML. A theoretically-guaranteed fast approximate solver and a new fuzzy bilevel learning framework will be developed to achieve the aim in complex situations; a methodology to transfer knowledge and an approach to fast-adapt bilevel optimization solutions when required computing resources change. The anticipated outcomes should significantly improve the reliability of ML with benefits for safety learning and computing resource optimisation in ML-based data analytics.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
The many lives and deaths of high redshift massive quiescent galaxies. This Fellowship will investigate the recent discovery of very massive, extremely early forming quiescent galaxies and explain their exceptional origin, death, and ultimate place in the local Universe. It is a multidisciplinary project that seeks to produce new knowledge using high-performance computing, software engineering, and sophisticated data analysis techniques. Expected outcomes include novel and improved supercomputer ....The many lives and deaths of high redshift massive quiescent galaxies. This Fellowship will investigate the recent discovery of very massive, extremely early forming quiescent galaxies and explain their exceptional origin, death, and ultimate place in the local Universe. It is a multidisciplinary project that seeks to produce new knowledge using high-performance computing, software engineering, and sophisticated data analysis techniques. Expected outcomes include novel and improved supercomputer simulations of several billions of galaxies processed through a virtual observatory, providing tools and fundamental knowledge for observational, theoretical, and computational astrophysics.Read moreRead less
An intelligent condition-monitoring system for mineral screening machines. This project aims to develop an intelligent condition-monitoring system for screening machines which are widely used for classifying mineral particles in the mining industry. This project will develop new vibration-based methodologies and techniques for fault diagnostics and remaining useful life prediction of bearings and gears in situations with multiple complex sources and interferences. The monitoring system, as the e ....An intelligent condition-monitoring system for mineral screening machines. This project aims to develop an intelligent condition-monitoring system for screening machines which are widely used for classifying mineral particles in the mining industry. This project will develop new vibration-based methodologies and techniques for fault diagnostics and remaining useful life prediction of bearings and gears in situations with multiple complex sources and interferences. The monitoring system, as the expected outcomes of this project, will modernise the current maintenance practices towards condition-based predictive maintenance, reducing unplanned downtime, increasing productivity and reducing maintenance costs for the Australian mining industry. It will also add more value to the Australian manufactured products. Read moreRead less