Developing And Applying Statistical Genetics Methods To Identify Genes, Molecular Biomarkers And Environmental Agents That Causally Affect Risk Of Complex Musculoskeletal Diseases
Funder
National Health and Medical Research Council
Funding Amount
$707,370.00
Summary
My aim is to identify genes, biological molecules and environmental factors that causally affect risk of osteoporosis and ankylosing spondylitis (a form of autoimmune arthritis) using novel and existing statistical genetics methodologies. My research will advance understanding of the causes of these diseases, identify new opportunities for their treatment, and provide the scientific community with new statistical methods and software to identify factors that causally influence risk of disease.
Most eye diseases have a genetic contribution, whether rare disorders affecting children such as retinoblastoma or congenital cataracts through to common disorders of older people such as myopia, age-related macular degeneration or glaucoma. We will continue our successful research to find genes that cause these diseases and use this to improve patient care and prevent blindness. We will work out how families can use this genetic information to participate in trials to develop new treatments.
Discovery Early Career Researcher Award - Grant ID: DE230101033
Funder
Australian Research Council
Funding Amount
$420,154.00
Summary
Scalable and Lightweight On-Device Recommender Systems. This project aims to address the resource-intensive and non-resilient nature of existing cloud-based personalised recommendation services. This project expects to generate new knowledge in the intersection of on-device machine learning and recommender systems. The expected outcomes include a novel auto-deployment platform that can efficiently customise a model for each user device's configuration, supporting on-device recommendation and mod ....Scalable and Lightweight On-Device Recommender Systems. This project aims to address the resource-intensive and non-resilient nature of existing cloud-based personalised recommendation services. This project expects to generate new knowledge in the intersection of on-device machine learning and recommender systems. The expected outcomes include a novel auto-deployment platform that can efficiently customise a model for each user device's configuration, supporting on-device recommendation and model updates with tiny computational footprints. The benefits of these outcomes will position Australia at the forefront of AI and give numerous businesses the tools needed to deploy innovative business systems with a secure and cost-effective advantage.Read moreRead less
EEG Based Global Network Models and Platform for Brain States Assessment. This project aims to generate new knowledge and tools in global brain network modelling and deep learning technology. It addresses the significant issues in higher brain function state assessment using brain signal EEG. The project applies global brain networks to model brain dynamical activities as a whole, and assesses higher brain functions such as consciousness, fatigue, sleep, stress and depression, and their step by ....EEG Based Global Network Models and Platform for Brain States Assessment. This project aims to generate new knowledge and tools in global brain network modelling and deep learning technology. It addresses the significant issues in higher brain function state assessment using brain signal EEG. The project applies global brain networks to model brain dynamical activities as a whole, and assesses higher brain functions such as consciousness, fatigue, sleep, stress and depression, and their step by step evolution in real-time using innovative deep learning approaches. The expected outcomes are optimised brain network models and a platform technology. The intended results can be applied to greatly improve the sleep quality and productivity of general community, and the safety of workplace and transportation.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100816
Funder
Australian Research Council
Funding Amount
$444,000.00
Summary
Probing dark energy with the largest 3D Map of the Universe. Dark Energy is one of the most profound mysteries of modern physics. It makes up about 70 percent of the Universe, but no compelling theory can explain its nature. This project aims to measure the properties of Dark Energy with unprecedented accuracy: an order of magnitude better than the state of the art. It aims to accomplish this by extracting information from the largest 3D map of the cosmos, built with the optical spectra of 35 mi ....Probing dark energy with the largest 3D Map of the Universe. Dark Energy is one of the most profound mysteries of modern physics. It makes up about 70 percent of the Universe, but no compelling theory can explain its nature. This project aims to measure the properties of Dark Energy with unprecedented accuracy: an order of magnitude better than the state of the art. It aims to accomplish this by extracting information from the largest 3D map of the cosmos, built with the optical spectra of 35 million galaxies, observed by the Dark Energy Spectroscopic Instrument. This project will foster Australia's historic leadership and investments in galaxy surveys via unique international partnerships, and produce cutting-edge tools for big data analyses with important applications in a wide range of industries.Read moreRead less
Embracing Changes for Responsive Video-sharing Services. Video-sharing platforms are a critical information channel for the public. Increasing scale and shifts in user base, with Generation Z now as the dominant user, have resulted in an unprecedented amount of ubiquitous changes in the content and users of these platforms which greatly challenges the responsiveness and quality of the services provided. This project aims to design innovative algorithms to effectively predict and leverage changes ....Embracing Changes for Responsive Video-sharing Services. Video-sharing platforms are a critical information channel for the public. Increasing scale and shifts in user base, with Generation Z now as the dominant user, have resulted in an unprecedented amount of ubiquitous changes in the content and users of these platforms which greatly challenges the responsiveness and quality of the services provided. This project aims to design innovative algorithms to effectively predict and leverage changes, optimise the value of changes, and extract insights from changes for diverse downstream applications of video-sharing platforms. The expected outcomes will create new-generation representation learning techniques, and provide practical tools to amplify the socioeconomic values of video-sharing platforms.Read moreRead less
Temporal Graph Mining for Anomaly Detection. This project aims to develop new technologies to detect anomalous patterns from dynamic networked data. Anomalies in networked data are commonly seen but are often hidden within the complex interconnections of large-scale, heterogeneous, and dynamic data, rendering existing detection methods ineffective. This project expects to design novel temporal graph mining techniques to compress large-scale networks, unify heterogeneous information, and enable l ....Temporal Graph Mining for Anomaly Detection. This project aims to develop new technologies to detect anomalous patterns from dynamic networked data. Anomalies in networked data are commonly seen but are often hidden within the complex interconnections of large-scale, heterogeneous, and dynamic data, rendering existing detection methods ineffective. This project expects to design novel temporal graph mining techniques to compress large-scale networks, unify heterogeneous information, and enable label-efficient anomaly detection. The performance will be assessed in social and business networks, with significant benefits to governments and businesses in many critical applications, including cyberbullying detection, malicious account detection, and cyber-attack detection.Read moreRead less
Responsible modelling respecting privacy, data quality, and green computing. With the unprecedented growing impact of data on science, the economy and society, there comes the need for responsible data science practices which are accountable for the social good. This project aims to investigate the challenging problem of how to provide responsible data management, spanning across privacy-aware data exploration, resilient modelling to cope with imperfect data, and efficient model architectures fo ....Responsible modelling respecting privacy, data quality, and green computing. With the unprecedented growing impact of data on science, the economy and society, there comes the need for responsible data science practices which are accountable for the social good. This project aims to investigate the challenging problem of how to provide responsible data management, spanning across privacy-aware data exploration, resilient modelling to cope with imperfect data, and efficient model architectures for resource-constrained environments. This will be achieved by developing theories and techniques for complex real-world multi-modal data retrieval throughout the data life-cycle. The expected outcomes will significantly contribute to building capability in emerging technologies in the context of responsible data science. 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