Discovery Early Career Researcher Award - Grant ID: DE160100007
Funder
Australian Research Council
Funding Amount
$303,000.00
Summary
The Future of Urban Routing and Navigation. This project aims to develop new efficient techniques for mixed-initiative routing in large transportation networks. Current state-of-the-art techniques for real-world journey planning take user requirements as input and generate a few proposed journeys as output. However, the most useful decision-support systems are mixed-initiative: the Information Technology (IT) system and user work together to find the best decisions. In the context of journey pla ....The Future of Urban Routing and Navigation. This project aims to develop new efficient techniques for mixed-initiative routing in large transportation networks. Current state-of-the-art techniques for real-world journey planning take user requirements as input and generate a few proposed journeys as output. However, the most useful decision-support systems are mixed-initiative: the Information Technology (IT) system and user work together to find the best decisions. In the context of journey planning, interaction with the user is needed to find the best combination of private, public and active transportation; understand trade-offs between cost, starting time, journey time, convenience and reliability; and react to delays and disruptions. This project aims to develop dynamic decision-support systems that will help travellers reach their destinations cheaper, faster and more conveniently.Read moreRead less
Temporal and spatial Bayesian network modelling for improved fog forecasting. This project aims to improve the accuracy of fog forecasting by explicitly modelling the spatial and temporal uncertainties surrounding fog formation. It is expected weather forecast services will adopt our approach to improve their predictions of fog, which will in turn help transport companies save costs, cut emissions and improve safety.
Increasing data quality with group associations in outsourcing environments. Outsourcing of data storage is increasingly common, but poses major problems for data utility and confidentiality. This project aims to discover how tuples (data structures) in fragments can be grouped to increase the utility of queries executed over fragments. The project will create a framework that satisfies information protection goals while achieving utility for queries. The developed algorithms and techniques will ....Increasing data quality with group associations in outsourcing environments. Outsourcing of data storage is increasingly common, but poses major problems for data utility and confidentiality. This project aims to discover how tuples (data structures) in fragments can be grouped to increase the utility of queries executed over fragments. The project will create a framework that satisfies information protection goals while achieving utility for queries. The developed algorithms and techniques will formally specify and develop a model to validate loose association rules while minimising data leakage risks. The outcomes will benefit Australians through enabling sharing and linking increasingly large datasets securely and cheaply.Read moreRead less
Advanced planning systems for vertically integrated supply chain management. This project will integrate various algorithms into an adaptive, dynamic and intelligent system that deals with the vertically integrated supply chains. The outcomes include publications in the quality outlets, generation of intellectual property, and dissemination of this research amongst the research and business communities.
Data mining complex transactional and criminal networks. Money laundering, if undetected, poses a major concern for governments and communities. The software system platform for detecting money laundering networks from this project will be the first that can assist intelligence data analysts to detect unknown money laundering networks faster and more accurately, helping fight crimes more efficiently.
Discovery Early Career Researcher Award - Grant ID: DE200101310
Funder
Australian Research Council
Funding Amount
$426,918.00
Summary
Dimension-reduced Reinforcement Learning for Large-scale Fleet Management. This project aims to address the problems in large-scale fleet management to ensure the efficiency of tomorrow’s transportation models, such as on-demand ride-hailing and mobility-as-a-service. The expected outcomes of this project include improved techniques for optimising the utility of large fleets of vehicles, and particularly robust dimension-reduced reinforcement learning algorithms that are capable of handling the ....Dimension-reduced Reinforcement Learning for Large-scale Fleet Management. This project aims to address the problems in large-scale fleet management to ensure the efficiency of tomorrow’s transportation models, such as on-demand ride-hailing and mobility-as-a-service. The expected outcomes of this project include improved techniques for optimising the utility of large fleets of vehicles, and particularly robust dimension-reduced reinforcement learning algorithms that are capable of handling the complex dynamics of supply and demand in transportation. The results should advance both research and technology in academia and the transportation industry and will also provide significant benefits to Australia and the international community by enhancing the energy-efficiency of and access to the mobility of the future.Read moreRead less
Smart micro learning with open education resources. This project aims to enhance personalised learning systems for mobile device users . Open online education is gaining in popularity with its ease of use. The project tackles the problems in relation to more and more popular mobile and ‘micro learning’, where people learn on the move and within small units of time. Ontology and machine learning technologies used in this project will help to optimise the offering of open education resources, by p ....Smart micro learning with open education resources. This project aims to enhance personalised learning systems for mobile device users . Open online education is gaining in popularity with its ease of use. The project tackles the problems in relation to more and more popular mobile and ‘micro learning’, where people learn on the move and within small units of time. Ontology and machine learning technologies used in this project will help to optimise the offering of open education resources, by providing solutions meeting each individual learner’s needs. The main outcome will consolidate a cloud based micro learning framework through integrating a group of novel algorithms.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100274
Funder
Australian Research Council
Funding Amount
$415,675.00
Summary
Graph Neural Networks for Efficient Decision-making towards Future Grids. This project aims to develop a breakthrough framework for decision-focused learning by integrating explainable graph neural networks and efficient computational methods. It expects to create new methodologies of graph representation learning for unlocking data insight with spatiotemporal knowledge while to build new accelerated optimisation theories for speeding up decision-focused learning model. The expected outcomes wil ....Graph Neural Networks for Efficient Decision-making towards Future Grids. This project aims to develop a breakthrough framework for decision-focused learning by integrating explainable graph neural networks and efficient computational methods. It expects to create new methodologies of graph representation learning for unlocking data insight with spatiotemporal knowledge while to build new accelerated optimisation theories for speeding up decision-focused learning model. The expected outcomes will advance big spatiotemporal data analytics and nonlinear optimisation theory for solving decision-making tasks towards a future energy system. This should promote the Australian power industry transition to a sustainable future grid based on a digitalisation approach to efficient energy management against climate changes.Read moreRead less
Fuzzy Transfer Learning for Prediction in Data-Shortage and Rapidly-Changing Environments. Collecting sufficient up-to-date data to train a learning model for data analysis and prediction is difficult and expensive. This project will develop a Fuzzy Transfer Learning methodology, using Information Granularity theory, that exploits data with different features and/or distributions available in other, similar systems, to provide accurate learning-based prediction for current problems. It will esta ....Fuzzy Transfer Learning for Prediction in Data-Shortage and Rapidly-Changing Environments. Collecting sufficient up-to-date data to train a learning model for data analysis and prediction is difficult and expensive. This project will develop a Fuzzy Transfer Learning methodology, using Information Granularity theory, that exploits data with different features and/or distributions available in other, similar systems, to provide accurate learning-based prediction for current problems. It will establish a new research direction, Fuzzy Transfer Learning for Prediction, and the outcomes will enable government and industry to better use past experience to make more accurate predictions and decisions. Highly significant benefits will also accrue in the data analytics, business intelligence and decision making research fields.Read moreRead less
Next generation Customer Relationship Management (CRM) framework through intelligence and relationships from multiple information sources. In the current competitive times, organisations need to improve their service delivery and customer care processes and provide customised service to customers. This project develops a Customer Relationship Management (CRM) framework that can extract intelligence and relationships from multiple data sources to improve customer management and satisfaction in pu ....Next generation Customer Relationship Management (CRM) framework through intelligence and relationships from multiple information sources. In the current competitive times, organisations need to improve their service delivery and customer care processes and provide customised service to customers. This project develops a Customer Relationship Management (CRM) framework that can extract intelligence and relationships from multiple data sources to improve customer management and satisfaction in public and private organisations.Read moreRead less