Discovery Early Career Researcher Award - Grant ID: DE210101808
Funder
Australian Research Council
Funding Amount
$395,775.00
Summary
Genetic Programming for Big Data Analytics. The project aims to extend a powerful machine learning method, called genetic programming and also developing a new concept called Alpha program, for big data analytics. This project expects to generate a new approach by finding a systematic approach to develop gene structures using information theory. By borrowing the best genes from the population of programs, the Alpha program concept will be developed for the first time. The proposed approach aims ....Genetic Programming for Big Data Analytics. The project aims to extend a powerful machine learning method, called genetic programming and also developing a new concept called Alpha program, for big data analytics. This project expects to generate a new approach by finding a systematic approach to develop gene structures using information theory. By borrowing the best genes from the population of programs, the Alpha program concept will be developed for the first time. The proposed approach aims to enhance genetic programming for many practical problems. I contend that not only finding better tools for big data analytics is in the best interest of machine learning and big data communities, it also provides significant benefits for other communities and industries in Australia.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210101458
Funder
Australian Research Council
Funding Amount
$387,141.00
Summary
Scalable and Deep Anomaly Detection from Big Data with Similarity Hashing. Anomaly detection, aiming to identify anomalous but insightful patterns in data mining, is an important big data analytics technique. The nature of big data requires a detection method that can handle fast-evolving data of diverse types. However, existing methods suffer from either high computational cost or low detection performance. This project aims to develop a detection framework to advance detection performance and ....Scalable and Deep Anomaly Detection from Big Data with Similarity Hashing. Anomaly detection, aiming to identify anomalous but insightful patterns in data mining, is an important big data analytics technique. The nature of big data requires a detection method that can handle fast-evolving data of diverse types. However, existing methods suffer from either high computational cost or low detection performance. This project aims to develop a detection framework to advance detection performance and efficiency, based on a novel deep learning model called deep isolation forest which is different from the traditional artificial neural network based models. The outcome will bring huge benefits to various applications such as real-time predictive maintenance in smart manufacturing, and intrusion detection in cybersecurity.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120100995
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Visual navigation for sunny summer days and stormy winter nights. This project will develop innovative techniques for camera-based navigation that recognise locations under a wide range of environmental conditions caused by day-night cycles, weather and seasonal change. These techniques will enable the widespread use of cheap and lightweight cameras in robot and personal navigation systems.
Discovery Early Career Researcher Award - Grant ID: DE130100292
Funder
Australian Research Council
Funding Amount
$285,000.00
Summary
Towards a stronger proof system for combinatorial optimisation. Combinatorial optimisation problems such as staff rostering, vehicle routing or resource allocation are central to the efficiency of many businesses and industries. This project will improve optimisation technology by using the low-level structure of the problems to find better solutions. This will save time, money and reduce environmental impact.
Discovery Early Career Researcher Award - Grant ID: DE150100104
Funder
Australian Research Council
Funding Amount
$330,000.00
Summary
Towards transforming data streams into real-time knowledge. This project aims to address a key problem of interpreting and providing meaningful information in real-time from large volumes of multivariate, noisy and incomplete data in fine-scale monitoring applications. Specifically, it targets air quality monitoring within a workplace. The project aims to significantly advance the current models for online data clustering and real-time anomaly detection in streaming data. The project aims to pro ....Towards transforming data streams into real-time knowledge. This project aims to address a key problem of interpreting and providing meaningful information in real-time from large volumes of multivariate, noisy and incomplete data in fine-scale monitoring applications. Specifically, it targets air quality monitoring within a workplace. The project aims to significantly advance the current models for online data clustering and real-time anomaly detection in streaming data. The project aims to produce computational models for the two aforementioned tasks and a complete system prototype for indoor air quality monitoring. This system has major health benefits for workers and the showcased computational models have various industrial potentials with significant socio-economic benefits to Australia.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE150101351
Funder
Australian Research Council
Funding Amount
$315,000.00
Summary
Playing and Solving General Games. Constructing rational agents for general dynamic decision problems is a long-standing open Artificial Intelligence challenge. An important milestone is to construct artificial agents that can learn and play new games well (universal playing agents). Specialised artificial intelligence systems are increasingly successful in domains such as Chess, Go, and Poker. The project aims to develop the theoretical and practical foundations of universal playing agents thro ....Playing and Solving General Games. Constructing rational agents for general dynamic decision problems is a long-standing open Artificial Intelligence challenge. An important milestone is to construct artificial agents that can learn and play new games well (universal playing agents). Specialised artificial intelligence systems are increasingly successful in domains such as Chess, Go, and Poker. The project aims to develop the theoretical and practical foundations of universal playing agents through a mathematical study of algorithms and heuristics for specific games. This project aims to significantly bridge the gap from efficient specialised players to high performance rational agents.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100964
Funder
Australian Research Council
Funding Amount
$427,068.00
Summary
Early Prediction in Large-scale Time-variant Information Networks. This project aims to develop an early prediction system to predict possible outbreaks of malicious messages in time-variant information networks. The research will primarily leverage deep representations of time-variant subsequence and substructure patterns in large-scale social networks to signal malicious and malevolent messages before it has a chance to propagate. This project will lay the theoretical foundations of this emerg ....Early Prediction in Large-scale Time-variant Information Networks. This project aims to develop an early prediction system to predict possible outbreaks of malicious messages in time-variant information networks. The research will primarily leverage deep representations of time-variant subsequence and substructure patterns in large-scale social networks to signal malicious and malevolent messages before it has a chance to propagate. This project will lay the theoretical foundations of this emerging field to strengthen Australia’s world leadership role in data science. Practically, the novel theories and data analytics technologies developed will help to safeguard Australian business, industry, and society from cyberfraud, online rumour-mongering, and financial loss.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130100771
Funder
Australian Research Council
Funding Amount
$278,780.00
Summary
Automated system for bone texture analysis of osteoarthritis in hand radiographs. Early detection and prediction of hand osteoarthritis are not feasible by current methods of visual grading of hand radiographs. This project will develop a novel, fully automated system for selection of bone texture regions on hand radiographs and their analysis to address this problem.
Discovery Early Career Researcher Award - Grant ID: DE220101390
Funder
Australian Research Council
Funding Amount
$402,900.00
Summary
Towards Human-like Machine Perception for Embodied AI. This project aims to investigate human-like visual perception, whereby AI machines can see and interpret the world like a human. The expected outputs will empower AI machines with the abilities of human-centered visual recognition and annotation-efficient learning through a set of deep learning techniques, and the ability to actively gather visual information through a reinforcement learning methodology (for decision support). This research ....Towards Human-like Machine Perception for Embodied AI. This project aims to investigate human-like visual perception, whereby AI machines can see and interpret the world like a human. The expected outputs will empower AI machines with the abilities of human-centered visual recognition and annotation-efficient learning through a set of deep learning techniques, and the ability to actively gather visual information through a reinforcement learning methodology (for decision support). This research is fundamental to the creation of embodied AI machines, which are expected to provide assistance to humans in industry, education and health. It thus will indicate immediate applications embracing autonomous vehicles and domestic robotics, providing scientific, social and economic benefits for Australia.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120101161
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Compressive sensing based probabilistic graphical models (PGM). The aim of the project is to develop fast, large scale probabilistic graphical models (PGM) learning and inference methods. The resulting system will be able to process large scale PGMs on a standard PC, and will be easily extendable to computer clustering for larger scale PGMs requiring higher precision.