From fundamental research to societal impact, our research into artificial intelligence (AI) delivers impact across a range of industries. We develop state-of-the-art techniques in key AI areas such as machine learning, computer vision, robotics, evolutionary computation, optimisation, multi-agent systems, natural language processing, predictive analysis, and virtual reality. We apply those techniques to solve challenging problems in health, agriculture, biology, energy, education, mining, cybersecurity, defence, space, and other areas of high importance to society.
Machine Learning Foundations: Focused on theoretical principles that govern how AI systems and their models learn, generalise and remain robust in complex environments, from investigating large language models to statistical learning theory and optimisation algorithms. This research can be applied to healthcare, software engineering, defence and autonomous systems – areas where understanding is as important as performance.
AI-based optimisation: Focused on finding intelligent solutions to complex problems, particularly in dynamic environments and where multi-objectivity exists. This research improves trust and reliability in AI-based optimisation methods.
Knowledge discovery and responsible AI: Addressing fairness, transparency and accountability, building trust in AI models especially in high stakes environments such as medicine, healthcare and defence.
Computer vision and multimodal perception: Combining geometric modelling with machine learning to interpret visual information. Our research can be applied to improve autonomous systems, defence systems and medical imaging, where application of visual data is critical.
Natural language processing and data fusion: Focused on extracting meaning from text using learning-based methods, often in specialised domains, where diverse data streams must be synthesised into actionable insights.
Knowledge representation and hybrid AI: Combining symbolic reasoning with data-driven learning to build systems that learn from data and reason with structured knowledge. Hybrid AI enables intelligent decision support systems that operate effectively in agriculture, manufacturing and maintenance.
Robotics, sensing, and embedded intelligence: Intelligent systems that interact with the physical world by combining sensing technologies with embedded computation, such as remote sensing and signal processing. Applications include smart environments, tracking and monitoring systems, and various defence settings.
Reinforcement learning: Examining how agents learn through interaction with dynamic or uncertain environments, supporting the build of systems that learn over time and adapt to changing conditions. This research is valuable in defence contexts, as well as robotics and autonomous satellite systems.
Multi-agent systems and autonomous decision-making: How do multiple AI agents interact, coordinate and make decisions in shared and contested environments? Our research examines factors including agent behaviour, distributed reasoning and mechanisms for cooperation and negotiation, with application in enterprise systems, autonomous fleets and organisational workflows.
Human-centred AI and software intelligence: Designing systems that are understandable, useable and aligned with human needs. Our research can augment human ability in a range of contexts, including engineering, manufacturing and cybersecurity.
