Discovery Early Career Researcher Award - Grant ID: DE240100827
Funder
Australian Research Council
Funding Amount
$458,737.00
Summary
Delineating the developmental requirements for stem-like T cells. Stem-like CD8 T cells are critical for sustaining long-term systemic T cell activity. The signalling required for their development, however, remains elusive. Integrating multidisciplinary expertise, cutting-edge technology and highly innovative methods, this project aims to define the signalling cues provided by tissue microenvironment that control the development and maintenance of stem-like T cells, and thereby dictate systemic ....Delineating the developmental requirements for stem-like T cells. Stem-like CD8 T cells are critical for sustaining long-term systemic T cell activity. The signalling required for their development, however, remains elusive. Integrating multidisciplinary expertise, cutting-edge technology and highly innovative methods, this project aims to define the signalling cues provided by tissue microenvironment that control the development and maintenance of stem-like T cells, and thereby dictate systemic immunity. This project is expected to generate fundamental knowledge on basic immunology and T cell biology, which can benefit the academic, public health and biotechnology sectors by enhancing the international standing of Australian research on basic immunology and fostering new commercial opportunities. Read moreRead less
Automatic detection and modelling of acoustic markers of speech timing. This project aims to create new automatic sensing, analysis and assessment of cognitive, affective, mental and physical state from voice for mobile and computing devices. This project expects to generate new understanding of the effects of these states on detailed timing indicators of speech motor control, and new signal processing and machine learning methods that best exploit it. Expected outcomes from this project include ....Automatic detection and modelling of acoustic markers of speech timing. This project aims to create new automatic sensing, analysis and assessment of cognitive, affective, mental and physical state from voice for mobile and computing devices. This project expects to generate new understanding of the effects of these states on detailed timing indicators of speech motor control, and new signal processing and machine learning methods that best exploit it. Expected outcomes from this project include a new and accurate deep neural network framework for learning, analysing and detecting human states from speech automatically using articulatory timing markers. This should provide significant benefits, such as individually-tailored, frequent and low-cost automatic detection, monitoring and analytics for adverse states.Read moreRead less
Molecular Diagnosis And Therapy Of Autoimmune Disease Using Translational And Reverse Translational Approaches
Funder
National Health and Medical Research Council
Funding Amount
$2,331,372.00
Summary
We plan to translate our recent discoveries on human gene variants and molecules produced by immune cells (follicular T cells) into effective therapies for autoimmune diseases. This will involve understanding the mechanisms by which the genes and molecules regulate immune tolerance, stratifying patients with autoimmune disease using newly identified biomarkers, trialling existing biologicals according to affected molecular pathway, and taking novel targets through to commercialisation.
Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an ....Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an open-source tool that can capture precision correlations between deep code features and diverse vulnerabilities to pinpoint emerging vulnerabilities without the need for bug specifications. Significant benefits include greatly improved quality, reliability and security for modern software systems.Read moreRead less
Adversarial Learning of Hybrid Representation. This project aims to design and implement a foundational deep representation learning framework for early detection, classification and defense of emerging malware by capturing their underlying behaviours via structured and unstructured heterogeneous information through hybrid representation learning, behaviour graph mining, and symbolic adversarial learning to discover and defend unknown malware families, thereby significantly boosting the accuracy ....Adversarial Learning of Hybrid Representation. This project aims to design and implement a foundational deep representation learning framework for early detection, classification and defense of emerging malware by capturing their underlying behaviours via structured and unstructured heterogeneous information through hybrid representation learning, behaviour graph mining, and symbolic adversarial learning to discover and defend unknown malware families, thereby significantly boosting the accuracy and robustness of existing classifiers and detectors. The resulting representation learning framework will enhance the national security to protect user privacy, reducing the multi-million-dollar loss caused by fraudulent transactions, and defending against cyber attacks.Read moreRead less
Poly(amino acids) as immune stimulators. This project aims to develop nanoparticles built from natural hydrophobic amino acids as an immune stimulatory delivery system for peptide antigens. Currently available immune stimulants (adjuvants) are often toxic and/or are poorly chemically defined fragments of bacteria or toxins and vary from batch-to-batch. New adjuvants are in high demand; especially to facilitate the use of optimal, but weakly immunogenic, peptide antigens. It is expected that the ....Poly(amino acids) as immune stimulators. This project aims to develop nanoparticles built from natural hydrophobic amino acids as an immune stimulatory delivery system for peptide antigens. Currently available immune stimulants (adjuvants) are often toxic and/or are poorly chemically defined fragments of bacteria or toxins and vary from batch-to-batch. New adjuvants are in high demand; especially to facilitate the use of optimal, but weakly immunogenic, peptide antigens. It is expected that the proposed project will develop a novel efficient, safe and notably biodegradable self-adjuvanting delivery system that can be fully customised to match an antigen of choice. This foundational research should provide important advances for commercial immune stimulatory applications.Read moreRead less
Fairness aware data mining for discrimination free decision-making. This project aims to develop data mining methods to detect algorithmic discriminations and to build fair decision models. It expects to provide techniques for regulatory organisations to detect discriminations in algorithmic decisions, and for various companies and organisations to build fair decision systems. Expected outcomes are novel and accurate methods for discrimination detection, practical and versatile techniques for fa ....Fairness aware data mining for discrimination free decision-making. This project aims to develop data mining methods to detect algorithmic discriminations and to build fair decision models. It expects to provide techniques for regulatory organisations to detect discriminations in algorithmic decisions, and for various companies and organisations to build fair decision systems. Expected outcomes are novel and accurate methods for discrimination detection, practical and versatile techniques for fair decision model building, and improved understanding of the relationships between privacy preservation and discrimination prevention to enable new techniques to achieve both goals. The developed techniques enable society to tackle ethical challenges in the big data era where many decisions are analytics based. Read moreRead less
Damage Detection and Quantification using Infrastructure Digital Twins. Structural health monitoring is vital for infrastructure assets management as early detection of structural conditions is key to both safety and ongoing maintenance. This project combines computer vision, vibration tests, finite element modelling and deep learning technologies to develop an efficient structural health monitoring system. Digital twins created from images taken by cameras or UAVs will be correlated through dee ....Damage Detection and Quantification using Infrastructure Digital Twins. Structural health monitoring is vital for infrastructure assets management as early detection of structural conditions is key to both safety and ongoing maintenance. This project combines computer vision, vibration tests, finite element modelling and deep learning technologies to develop an efficient structural health monitoring system. Digital twins created from images taken by cameras or UAVs will be correlated through deep learning with structural conditions and load-carrying capacities obtained from vibration tests and finite element model analysis for efficient structural damage detection and quantification. The project will lead to effective structural health monitoring and enhance structural safety and reduce maintenance costs. Read moreRead less
DeepHoney: Automatic Honey Data Generation for Active Cyber Defence . This project aims to enhance the security of networks and information systems by empowering them with intelligent deception techniques to achieve proactive attack detection and defence. In recent times, the fictitious environment – honeypot designed by human experience becomes popular to attract attackers and capture their interactions. However, rules-based construction of honeypots fails in preserving the privacy, boosting th ....DeepHoney: Automatic Honey Data Generation for Active Cyber Defence . This project aims to enhance the security of networks and information systems by empowering them with intelligent deception techniques to achieve proactive attack detection and defence. In recent times, the fictitious environment – honeypot designed by human experience becomes popular to attract attackers and capture their interactions. However, rules-based construction of honeypots fails in preserving the privacy, boosting the attractiveness and evolving the system. The project expects to advance deep learning and yield novel DeepHoney technologies with associated publications and open-source software. This should benefit science, society, and the economy by building the next generation of active cyber defence systems. Read moreRead less