This theme drives advances in the algorithms and theories that enable smart machines. Our researchers develop novel machine learning methods and principled AI techniques to enhance the reliability and trustworthiness of intelligent systems. By improving how machines learn from data – and ensuring they can generalise and adapt – we lay the foundations for AI innovations across many fields. From fundamental theory to cutting-edge applications, our work in AI and machine learning fuels breakthroughs in areas such as healthcare, finance, cybersecurity and environmental modelling.
Our research spans three strengths across multidisciplinary research
Our research aims to create smarter, more efficient ways for computers to learn and make decisions. This aligns with our broader strategy to lead in digital innovation and address national priorities in areas such as healthcare, infrastructure, and autonomous systems. This theme supports our mission to solve real-world problems through interdisciplinary collaboration and cutting-edge research.
We are developing new algorithms that improve the performance and reliability of machine learning systems. This includes work on deep neural networks for computer vision, reinforcement learning for autonomous decision-making, and data mining techniques for uncovering insights from large datasets. These innovations are applied in diverse domains such as robotics, software engineering, and biomedical diagnostics. Planning and control algorithms are being used to guide autonomous vehicles, while predictive models support early disease detection.
This research aims to improve intelligent systems that learn from complex data, with a focus on solving real-world challenges in areas like healthcare, transport, and audio processing, by developing advanced machine learning methods such as graph neural networks and transfer learning. This leads to smarter medical diagnoses, more efficient traffic systems, and clearer voice recognition, enhancing everyday experiences through faster services, better decision-making, and more responsive technologies.
Professor Qing Li, Professor Professor Philip Leong, Associate Professor Tongliang Liu, Associate Professor Chang Xu, Dr Zengxia Pei
Crux ML, DSTG
Our research aims to harness advanced computational methods, artificial intelligence, and data analytics to support intelligent, evidence-based decisions across diverse domains. This aligns with our strategic priorities in digital transformation, smart technologies, and interdisciplinary innovation. The goal is to develop scalable, trustworthy, and interpretable systems that can process complex, high-volume data to inform decisions in real time, improving outcomes in sectors such as healthcare, energy, infrastructure, and education.
We are developing advanced machine learning and algorithmic techniques, including graph neural networks, transfer learning, and multi-scale inference, to build intelligent systems that support data-driven decision-making. Our work spans software-hardware co-design and cloud-edge computing integration, ensuring scalable and high-performance solutions.
This research aims to improve intelligent, data-driven decision-making systems with a focus on processing complex, high-volume data across diverse domains, by developing scalable and trustworthy AI and algorithmic methods such as graph neural networks, transfer learning, and cloud-edge computing. This enables faster, more accurate decisions in areas like healthcare, transport, and infrastructure, leading to everyday benefits such as quicker medical diagnoses, safer traffic systems, and more responsive digital services.
Professor Joachim Gudmusson, Professor Seokhee Hong, Professor Judy Kay, Professor Albert Zomaya, Associate Professor Tongliang Liu, Associate Professor Simon Poon, Associate Professor Chang Xu, Professor Yonghui Li, Dr Neda Mohammadi, Dr Jeremy Qiu
Our research in algorithms and artificial intelligence (AI) aims to develop foundational technologies that power intelligent systems capable of solving complex, real-world problems. This includes designing efficient algorithms for data processing, optimisation, and decision-making, and advancing AI methods that learn, adapt, and interact with dynamic environments. These efforts align with our strategic focus on digital transformation, smart technologies, and leadership in data science and systems engineering.
We are developing cutting-edge techniques such as graph neural networks, transfer learning, and multi-scale inference, alongside software-hardware co-design and cloud-edge computing integration. We foster interdisciplinary collaboration and apply these technologies to practical challenges, like improving medical diagnostics, enhancing traffic systems, and refining audio processing. Our algorithms research complements this by focusing on scalable, efficient solutions for data-driven decision-making.
This research aims to improve intelligent data-driven systems with a focus on solving complex real-world problems, by developing advanced algorithms and AI techniques such as graph neural networks, transfer learning, and cloud-edge computing. This enables faster medical diagnoses, smarter traffic management, and better voice recognition, bringing everyday benefits like quicker healthcare, safer commutes, and more intuitive digital assistants.
Professor Albert Zomaya, Associate Professor Tongliang Liu, Associate Professor Chang Xu, Dr Sasha Rubin
