Interactions of Human and Machine Intelligence in Modern Economic Systems. Much of modern economic systems are driven by machine-machine and machine-human interactions that happens rapidly at large scale. But such interactions are often opaque and can have negative or catastrophic consequences, such as market plunges with no apparent economic reasons in financial trading, content recommendations that promote extremism, algorithms in gig economy leading to worker exploitation and wasted resources ....Interactions of Human and Machine Intelligence in Modern Economic Systems. Much of modern economic systems are driven by machine-machine and machine-human interactions that happens rapidly at large scale. But such interactions are often opaque and can have negative or catastrophic consequences, such as market plunges with no apparent economic reasons in financial trading, content recommendations that promote extremism, algorithms in gig economy leading to worker exploitation and wasted resources. This project aims for new theoretical results and algorithms at the intersection computational economics, game theory, and dynamical systems, that establish conditions under which the economic systems are stable, propose mechanisms that make the interactions more fair, transparent and aligned with human values.Read moreRead less
Computational Mechanisms of Online Attention Markets. The internet has operated as an major exchange of information and attention for the past few decades, yet surprisingly little is known about how individual choices and collective attention interact, let alone about how different parties can influence or control it. This project aims to uncover the mathematical underpinnings between individual actions and collective trends in online attention market, design computational methods for estimating ....Computational Mechanisms of Online Attention Markets. The internet has operated as an major exchange of information and attention for the past few decades, yet surprisingly little is known about how individual choices and collective attention interact, let alone about how different parties can influence or control it. This project aims to uncover the mathematical underpinnings between individual actions and collective trends in online attention market, design computational methods for estimating and influencing attention allocation, and enable applications where content consumers, producers, hosting platforms and regulatory bodies are each empowered with their share of influence in the attention market.Read moreRead less
3D Diffusion Models for Generating and Understanding 3D Scenes. Diffusion models, such as DALL-E2 and Imagen, have achieved remarkable success in generating photorealistic images and hold promise to solve long-standing computer vision problems. However, 3D scene generation remains unexplored. This research project aims to bridge the gap by developing 3D diffusion models capable of generating complete 3D scenes. This will advance our theoretical understanding of diffusion in complex 3D environmen ....3D Diffusion Models for Generating and Understanding 3D Scenes. Diffusion models, such as DALL-E2 and Imagen, have achieved remarkable success in generating photorealistic images and hold promise to solve long-standing computer vision problems. However, 3D scene generation remains unexplored. This research project aims to bridge the gap by developing 3D diffusion models capable of generating complete 3D scenes. This will advance our theoretical understanding of diffusion in complex 3D environments and open up new possibilities for applications in fields such as virtual reality, architecture, and city planning. The proposed 3D diffusion models will also enhance the accuracy of computer vision tasks related to 3D scene understanding, such as object detection, tracking, and semantic segmentation.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
Personalised Privacy-Preserving Network Data Publishing System . Data sharing has become a driving force for many businesses in industrial sectors. This project aims to develop a privacy preserving network data publishing system that can preserve user privacy in a personalised way while maintaining maximal utility of the published data. To make accurate privacy preservation, this project will design novel learning models to derive accurate users’ correlation and their privacy intention, develop ....Personalised Privacy-Preserving Network Data Publishing System . Data sharing has become a driving force for many businesses in industrial sectors. This project aims to develop a privacy preserving network data publishing system that can preserve user privacy in a personalised way while maintaining maximal utility of the published data. To make accurate privacy preservation, this project will design novel learning models to derive accurate users’ correlation and their privacy intention, develop efficient privacy preserving algorithms to deal with static and dynamic network data sharing. The success of this project will benefit many industries and government agencies to reduce users’ privacy breaches, avoid illegal consequences of sharing data, and enhance these service providers’ service quality.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100967
Funder
Australian Research Council
Funding Amount
$366,000.00
Summary
Open-world computer vision by detecting and tracking hierarchical objects. This project examines the problem of detecting and tracking objects using computer vision. A fundamental limitation of current algorithms is that they require labelled training data for every object class and therefore cannot be trusted to operate in unconstrained environments. This project aims to address this limitation using novel techniques that incorporate hierarchical relationships between object classes. Expected o ....Open-world computer vision by detecting and tracking hierarchical objects. This project examines the problem of detecting and tracking objects using computer vision. A fundamental limitation of current algorithms is that they require labelled training data for every object class and therefore cannot be trusted to operate in unconstrained environments. This project aims to address this limitation using novel techniques that incorporate hierarchical relationships between object classes. Expected outcomes include new paradigms for algorithm design and evaluation, and establishing the problem as a focus of international research. The key practical benefit would be to accelerate the wider deployment of visual perception in applications such as autonomous vehicles, interactive robotics, and video analysis.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH230100013
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub for Future Digital Manufacturing. This Hub aims to grow and accelerate Australian digital manufacturing (DM) transformation by devising novel DM technology and commercialisation/adoption pathways. The Hub expects to transform industry by developing novel AI and IoT-powered DM technology that provides for dramatic improvement in manufacturing productivity, resilience and competitiveness. Expected outcomes include novel DM technology for digitally representing, predicting, and imp ....ARC Research Hub for Future Digital Manufacturing. This Hub aims to grow and accelerate Australian digital manufacturing (DM) transformation by devising novel DM technology and commercialisation/adoption pathways. The Hub expects to transform industry by developing novel AI and IoT-powered DM technology that provides for dramatic improvement in manufacturing productivity, resilience and competitiveness. Expected outcomes include novel DM technology for digitally representing, predicting, and improving production and its outcomes via an open platform that supports reusing industry co-created DM solutions. Through supporting advanced manufacturing priorities and Industry 4.0, the Hub should provide significant benefits by increasing Australian manufacturing productivity and resilience by 30%.Read moreRead less
Learning to Reason in Reinforcement Learning. Deep Reinforcement Learning (RL) uses deep neural networks to represent and learn optimal decision-making policies for intelligent agents in complex environments. However, most RL approaches require millions of episodes to converge to good policies, making it difficult for RL to be applied in real-world scenarios taking significant resources. This project aims to equip RL with capabilities such as counterfactual reasoning and outcome anticipation to ....Learning to Reason in Reinforcement Learning. Deep Reinforcement Learning (RL) uses deep neural networks to represent and learn optimal decision-making policies for intelligent agents in complex environments. However, most RL approaches require millions of episodes to converge to good policies, making it difficult for RL to be applied in real-world scenarios taking significant resources. This project aims to equip RL with capabilities such as counterfactual reasoning and outcome anticipation to significantly reduce the number of interactions required, improve generalisation, and provide the agent with the capability to consider the cause-effects. These improvements would narrow the gap between AI and human capabilities and broaden the adoption of RL in real-world applications.Read moreRead less
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
Visual methods for advanced automation of underwater manipulation. This project will increase the autonomy of underwater robotic systems engaged in intervention and inspection tasks. Such activities are essential for the operation of subsea robotic systems used in offshore industries, scientific exploration and defence. Our approach will improve perception and situational awareness through the principled fusion of multiple navigation and camera sensors. We will use this improved scene understand ....Visual methods for advanced automation of underwater manipulation. This project will increase the autonomy of underwater robotic systems engaged in intervention and inspection tasks. Such activities are essential for the operation of subsea robotic systems used in offshore industries, scientific exploration and defence. Our approach will improve perception and situational awareness through the principled fusion of multiple navigation and camera sensors. We will use this improved scene understanding to effectively plan the motion of vehicles and manipulators through larger and more complex workspaces, enabling semi-supervised and autonomous task execution. Our project will demonstrate these capabilities in real-world deployments relevant to industry and marine science.Read moreRead less