A Bayesian Approach to Distributed Estimation for Multi-Object Systems. This project aims to develop new signal processing techniques that facilitate autonomous technologies for environmental perception, with the ability to efficiently process large data volumes from multiple sensing modalities. Rapid advances in sensors and networks have led to a digital data deluge, from which extracting useful information presents new technological challenges and opportunities. To address this development, th ....A Bayesian Approach to Distributed Estimation for Multi-Object Systems. This project aims to develop new signal processing techniques that facilitate autonomous technologies for environmental perception, with the ability to efficiently process large data volumes from multiple sensing modalities. Rapid advances in sensors and networks have led to a digital data deluge, from which extracting useful information presents new technological challenges and opportunities. To address this development, this project seeks to develop new distributed solutions for statistical estimation, which are specifically designed for dynamic systems with multiple object states, and are inherently scalable and robust. The potential benefits include new technologies for smart cities, autonomous infrastructure, and digital productivity.Read moreRead less
Data sharing with strong privacy against inference attacks. This project aims to develop theories and techniques for strong protection of personal information in sharing large datasets such as national health data or census records. It intends to achieve this through developing new information theoretic methods for synthesising datasets with proven high fidelity and protection against re-identification and inference attacks, where attackers try to learn probability of sensitive data. The expecte ....Data sharing with strong privacy against inference attacks. This project aims to develop theories and techniques for strong protection of personal information in sharing large datasets such as national health data or census records. It intends to achieve this through developing new information theoretic methods for synthesising datasets with proven high fidelity and protection against re-identification and inference attacks, where attackers try to learn probability of sensitive data. The expected outcomes are algorithms for public and private sector data curators to dial up or down their data access arrangements based on privacy risks and fidelity demands linked with different data types and uses. This project intends to enable Australians to securely benefit from valuable data in decision making.Read moreRead less
Parameter estimation for genetic time-series data: Theory and methods. This project aims to develop a novel computational framework for solving parameter estimation problems in evolutionary modelling by leveraging genetic time-series data measured by Next-Generation Sequencing technologies. It will foster international collaboration, cutting across disciplines. By introducing new techniques from signal processing and tools from random matrix theory commonly employed for mobile wireless communica ....Parameter estimation for genetic time-series data: Theory and methods. This project aims to develop a novel computational framework for solving parameter estimation problems in evolutionary modelling by leveraging genetic time-series data measured by Next-Generation Sequencing technologies. It will foster international collaboration, cutting across disciplines. By introducing new techniques from signal processing and tools from random matrix theory commonly employed for mobile wireless communications, it seeks to design scalable inference methods for resolving mutational fitness effects from genetic time-series measurements of complex evolving populations. This would enable new understanding of complex adaptive systems, such as pathogen evolution, host-immune dynamics, and acquisition of drug resistance. Read moreRead less
Deep Interaction Learning in Unlabelled Big Data and Complex Systems. This project aims to effectively model intricate interactions deeply embedded in unlabelled big data and complex systems, which are often hierarchical, heterogeneous, contextual, dynamic or even contrastive. Learning such interactions is the keystone of robust data science and for realizing the value of big data but it poses significant challenges and knowledge gaps to existing data analytics and learning systems. The expected ....Deep Interaction Learning in Unlabelled Big Data and Complex Systems. This project aims to effectively model intricate interactions deeply embedded in unlabelled big data and complex systems, which are often hierarchical, heterogeneous, contextual, dynamic or even contrastive. Learning such interactions is the keystone of robust data science and for realizing the value of big data but it poses significant challenges and knowledge gaps to existing data analytics and learning systems. The expected outcomes include new-generation theories and methods for the unsupervised learning of complex interactions in real-life big data, which are anticipated to enable the intrinsic processing of big data complexities and substantially enhance Australia’s leadership in frontier data science research and applications. Read moreRead less
Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-s ....Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-supervised learning that are robust to failures and provide explainable decisions for object detection and scene segmentation. The outcomes are expected to advance theory in robust deep learning and benefit 3D mapping, surveying, infrastructure monitoring, transport and robotics industries.Read moreRead less
Understanding the mechanisms of class B GPCR-transducer coupling. Current effort in developing drugs targeting G protein-coupled receptors (GPCRs) often result in low success rate due to the lack of understanding of the complexity and the spatiotemporal control of receptor function. The research program aims to understand the molecular mechanisms of receptor/transducer selectivity. The proposal integrated multi-disciplinary approaches to provide a deeper understanding of how the receptor is acti ....Understanding the mechanisms of class B GPCR-transducer coupling. Current effort in developing drugs targeting G protein-coupled receptors (GPCRs) often result in low success rate due to the lack of understanding of the complexity and the spatiotemporal control of receptor function. The research program aims to understand the molecular mechanisms of receptor/transducer selectivity. The proposal integrated multi-disciplinary approaches to provide a deeper understanding of how the receptor is activated responding to different ligands. The anticipated outcome including an enhanced capacity for understanding the fundamental biology, a stronger national and international collaborations. This will provide significant benefits including expanded basic knowledge and improvements in drug development efficiency. Read moreRead less
Declarative Networks: Towards Robust and Explainable Deep Learning. The aim of this project is to develop declarative machine learning techniques that exploit inherent structure and models of the world. Deep learning has become the dominant approach for machine learning with many products and promises built on this technology. But deep learning is expensive, opaque, brittle and relies solely on human labelled data. This project intends to make deep learning more reliable by establishing theory a ....Declarative Networks: Towards Robust and Explainable Deep Learning. The aim of this project is to develop declarative machine learning techniques that exploit inherent structure and models of the world. Deep learning has become the dominant approach for machine learning with many products and promises built on this technology. But deep learning is expensive, opaque, brittle and relies solely on human labelled data. This project intends to make deep learning more reliable by establishing theory and algorithms that allow physical and mathematical models to be embedded within a deep learning framework, providing performance guarantees and interpretability. This would likely benefit machine learning based products that can understand the world and interact with humans naturally through vision and language.Read moreRead less
Sensing biomechanical forces in the heart. Mechanosensitive ion channels are key molecules that define how each heart cell interacts with their physical environment. Yet how they enable cells to decode biomechanical cues remains poorly understood. At the heart of this problem is a lack of tools to quantify the force required for activation. This project aims to develop novel technologies to record the activity of these essential channels in a critical cell type within the heart, and use this inf ....Sensing biomechanical forces in the heart. Mechanosensitive ion channels are key molecules that define how each heart cell interacts with their physical environment. Yet how they enable cells to decode biomechanical cues remains poorly understood. At the heart of this problem is a lack of tools to quantify the force required for activation. This project aims to develop novel technologies to record the activity of these essential channels in a critical cell type within the heart, and use this information in addition to micro-engineering approaches to fully understand the role of these channels in force sensing and generation, at both the single cell and micro-tissue levels. This knowledge and technology has broad utility that extends far beyond cardiac biology into multiple fields.Read moreRead less
Automatic video annotation by learning from web data. This project aims to study next-generation video annotation technologies to automatically tag raw videos using a huge set of semantic concepts. The project will study new domain adaptation schemes and frameworks in order to substantially improve video annotation performance. The resulting prototype system can be directly used by ordinary users worldwide to search their personal videos using textual queries. The system is also applicable to vi ....Automatic video annotation by learning from web data. This project aims to study next-generation video annotation technologies to automatically tag raw videos using a huge set of semantic concepts. The project will study new domain adaptation schemes and frameworks in order to substantially improve video annotation performance. The resulting prototype system can be directly used by ordinary users worldwide to search their personal videos using textual queries. The system is also applicable to video surveillance applications, which can enhance Australia’s homeland security.Read moreRead less
The molecular basis for efficacy at G protein coupled receptors. This project aims to investigate the molecular steps underlying the relationship between sensing by signal-transmitting proteins on the cell surface called G protein-coupled receptors and cellular response. The project aims to build on studies that have sought to understand the primary, molecular basis for this cellular volume control. This project seeks to use these novel approaches to fill this knowledge gap, providing a deeper u ....The molecular basis for efficacy at G protein coupled receptors. This project aims to investigate the molecular steps underlying the relationship between sensing by signal-transmitting proteins on the cell surface called G protein-coupled receptors and cellular response. The project aims to build on studies that have sought to understand the primary, molecular basis for this cellular volume control. This project seeks to use these novel approaches to fill this knowledge gap, providing a deeper understanding of how physiology and medicines work. The project expects to expand fundamental understanding of signal transmission at this receptor class. This project will deliver benefits including expanded basic knowledge and a contribution to future improvements in drug development.Read moreRead less