Discovery Early Career Researcher Award - Grant ID: DE200101100
Funder
Australian Research Council
Funding Amount
$425,613.00
Summary
Towards Robotic Empathy: A human centred approach to future AI machines. The project aims to equip future robots with empathy by developing computational models which can leverage from verbal and non-verbal cues. With recent advances in artificial intelligence research, robots now have better cognitive and function skills, but they lack socio-emotional skills. Since these robots are expected to provide assistance to humans across different domains including rehabilitation, education and health c ....Towards Robotic Empathy: A human centred approach to future AI machines. The project aims to equip future robots with empathy by developing computational models which can leverage from verbal and non-verbal cues. With recent advances in artificial intelligence research, robots now have better cognitive and function skills, but they lack socio-emotional skills. Since these robots are expected to provide assistance to humans across different domains including rehabilitation, education and health care, empowering them with empathetic abilities is important for their success. The project will advance fundamental research in machine learning, affective computing and artificial intelligence to model human behavior, personality traits and emotions for an empathetic human-robot interaction.Read moreRead less
Improving the specificity of affective computing via multimodal analysis. This project aims to develop multimodal affective sensing techniques that can sense very subtle expressions in human moods and emotions. Much research in affective computing has investigated ways to improve the sensitivity of affect sensing approaches, resulting in more accurate estimates of affective states such as emotions or mood. What remains unsolved so far is the issue of specificity. This project will address this i ....Improving the specificity of affective computing via multimodal analysis. This project aims to develop multimodal affective sensing techniques that can sense very subtle expressions in human moods and emotions. Much research in affective computing has investigated ways to improve the sensitivity of affect sensing approaches, resulting in more accurate estimates of affective states such as emotions or mood. What remains unsolved so far is the issue of specificity. This project will address this issue through novel analyses of very subtle cues in facial and vocal expressions of affect embedded in a multimodal deep learning framework. Current approaches can successfully assist in binary classification tasks. This project will tackle the much more difficult problem of developing advanced affective sensing technology to simultaneously handle homogeneous and heterogeneous affect classes as well as continuous range estimates of affect intensity.Read moreRead less
A technology platform for multiple body site image-omics. The project aim is to derive a technology platform comprising new image processing and machine learning algorithms to integrate imaging and biological data across multiple body sites. The relationships between image features and biological data across multiple sites has not been discovered before. We propose the use of biological information from one sampled site to investigate other unsampled sites based on imaging-omics correspondences. ....A technology platform for multiple body site image-omics. The project aim is to derive a technology platform comprising new image processing and machine learning algorithms to integrate imaging and biological data across multiple body sites. The relationships between image features and biological data across multiple sites has not been discovered before. We propose the use of biological information from one sampled site to investigate other unsampled sites based on imaging-omics correspondences. We will use a data-driven, searchable graph model approach for knowledge discovery within the population data. The project will provide new insights into systems biology and bioinformatics that will then inform and promote benefits in life sciences, with potential future benefits in healthcare.Read moreRead less
Interpretable Behaviour Analysis with External Structured Knowledge. This project aims to develop novel interpretable neural models for predictive analytics tasks on human behaviour, operating on sequence behaviour data associated with external supportive structured knowledge. It is expected to present theoretical foundations for robust representation learning on heterogeneous behaviour data and interpretable machine reasoning models, which can support a broad scope of intelligent systems. Expec ....Interpretable Behaviour Analysis with External Structured Knowledge. This project aims to develop novel interpretable neural models for predictive analytics tasks on human behaviour, operating on sequence behaviour data associated with external supportive structured knowledge. It is expected to present theoretical foundations for robust representation learning on heterogeneous behaviour data and interpretable machine reasoning models, which can support a broad scope of intelligent systems. Expected outcomes will be a next-generation interpretable behaviour analysis system with versatile abilities to reason over various data structures and provide a high-level interpretability about its reasoning procedure. The benefits will span the research and industry sectors, e.g., retail, healthcare, service provider.Read moreRead less
Knowledge discovery and recommendation of multimedia data in healthcare. The project aims to develop tools to abstract/streamline the ever-growing information-rich multimedia contents into easily discoverable knowledges. Advanced multimedia knowledge graph will be first time developed to accurately exploit hidden knowledge for health industry, and served to generate right information recommendation for healthcare professionals (HCP) at the right time. The proposed technology will improve HCPs' c ....Knowledge discovery and recommendation of multimedia data in healthcare. The project aims to develop tools to abstract/streamline the ever-growing information-rich multimedia contents into easily discoverable knowledges. Advanced multimedia knowledge graph will be first time developed to accurately exploit hidden knowledge for health industry, and served to generate right information recommendation for healthcare professionals (HCP) at the right time. The proposed technology will improve HCPs' communication, keep them up to date, and enhance their speedy reaction to constantly changing situations/diseases, thus reducing poor patient outcomes and unnecessary hospital costs. It will make significant impact to a range of industries, e.g. healthcare, where personalised professional recommendation is demanded.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC170100030
Funder
Australian Research Council
Funding Amount
$4,133,659.00
Summary
ARC Training Centre in Cognitive Computing for Medical Technologies. The ARC Training Centre in Cognitive Computing for Medical Technologies aims to create a workforce that is expert in developing, applying and interrogating cognitive computing technologies in data-intensive medical contexts. This will facilitate the next generation of data-driven and machine learning-based medical technologies. The Centre will provide a world-class industry-driven research training environment for PhD students ....ARC Training Centre in Cognitive Computing for Medical Technologies. The ARC Training Centre in Cognitive Computing for Medical Technologies aims to create a workforce that is expert in developing, applying and interrogating cognitive computing technologies in data-intensive medical contexts. This will facilitate the next generation of data-driven and machine learning-based medical technologies. The Centre will provide a world-class industry-driven research training environment for PhD students and postdoctoral researchers. These researchers will lead the medical technology industry into a new era of data-driven personalised and precision medical devices and applications. The Centre will result in the development of capabilities in the core technologies of machine learning and the practical application of cognitive computing in the area of health.Read moreRead less
Personalised Learning for Per-pixel Prediction Tasks in Image Analysis. AI-assisted image segmentation & synthesis are very challenging and usually require pixel-level labelling (per-pixel prediction) that is costly to obtain. The small amount of labels makes it difficult to train an “optimal” unified model for varied data as conventional methods did. This project aims to develop a new paradigm “personalised learning” to tackle this problem, where each image could be dealt with a model tailored ....Personalised Learning for Per-pixel Prediction Tasks in Image Analysis. AI-assisted image segmentation & synthesis are very challenging and usually require pixel-level labelling (per-pixel prediction) that is costly to obtain. The small amount of labels makes it difficult to train an “optimal” unified model for varied data as conventional methods did. This project aims to develop a new paradigm “personalised learning” to tackle this problem, where each image could be dealt with a model tailored to individual characteristics. The success of this project could significantly advance the fundamental research in image analysis. Expected outcomes include new knowledge and algorithms for image analysis, which could benefit fields like biology and archaeology, where labeled images are hard to attain and scarce.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC210100019
Funder
Australian Research Council
Funding Amount
$4,583,816.00
Summary
ARC Training Centre for Optimal Ageing. The ARC Training Centre for Optimal Ageing aims to address issues identified by older adults as essential for quality of life. With our industry partners, we aim to train the next generation of researchers to understand, detect and improve psychosocial factors that support mental activity, physical health and social connectedness, and embrace advances in artificial intelligence, digital-enriched environments and adaptive workplaces to deliver effective dig ....ARC Training Centre for Optimal Ageing. The ARC Training Centre for Optimal Ageing aims to address issues identified by older adults as essential for quality of life. With our industry partners, we aim to train the next generation of researchers to understand, detect and improve psychosocial factors that support mental activity, physical health and social connectedness, and embrace advances in artificial intelligence, digital-enriched environments and adaptive workplaces to deliver effective digital solutions. By developing new capacity and capability to drive the digital transformation of industries supporting our ageing population, our Centre seeks to deliver economic and social benefits that enable Australians to live enriched, healthy and independent lives as they age.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101610
Funder
Australian Research Council
Funding Amount
$403,398.00
Summary
Towards Explainable Multi-source Multivariate Time-series Analysis. The aim of this project is to build deep learning models with transparent reasoning behind the results that can be easily interpreted by humans. The research rests on translating pertinent knowledge from multiple sources of complex data containing event sequences into graph form and embedding those knowledge graphs into a sophisticated deep learning model. Such an accomplishment represents the next great advance in machine intel ....Towards Explainable Multi-source Multivariate Time-series Analysis. The aim of this project is to build deep learning models with transparent reasoning behind the results that can be easily interpreted by humans. The research rests on translating pertinent knowledge from multiple sources of complex data containing event sequences into graph form and embedding those knowledge graphs into a sophisticated deep learning model. Such an accomplishment represents the next great advance in machine intelligence and will lay the theoretical foundations for building intelligent analysis tools that truly work in tandem with people. The potential benefits to science, society, and the Australian economy, particularly in finance, sensor technologies, and emergency health services would be appreciable.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101439
Funder
Australian Research Council
Funding Amount
$418,998.00
Summary
Towards a Reliable and Explainable Health Monitoring and Caring System. This project aims to unleash the power of deep learning on health monitoring and caring domain through a safe, reliable and explainable way. Its innovations lie on 1) developing a set of robust and explainable deep learning models that are guaranteed to be safe to complex environmental uncertainty; 2) designing an intelligent health monitoring and caring platform, powered by robust deep learning models, to better support the ....Towards a Reliable and Explainable Health Monitoring and Caring System. This project aims to unleash the power of deep learning on health monitoring and caring domain through a safe, reliable and explainable way. Its innovations lie on 1) developing a set of robust and explainable deep learning models that are guaranteed to be safe to complex environmental uncertainty; 2) designing an intelligent health monitoring and caring platform, powered by robust deep learning models, to better support the home-based health monitoring and caring for the elderly. The result will enable end-users to trust the decisions of deep learning models in safety-critical systems and significantly contribute to Australian aging society and national healthcare economy.Read moreRead less