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AI & ML Systems

Overview of theme

Research in AMS division focuses on developing AI & ML systems that operate across modalities, scales and real-world environments. This involves designing scalable and efficient learning algorithms that support adaptation and unlearning; creating and optimising large generative, language- and multimodal ML models; building streaming, spatio-temporal and graph-based learning architectures; embedding human-centred, ethical and trustworthy AI in real-world use-cases; applying ML techniques for human behaviour, health and ubiquitous systems; and exploring cyber-physical, embodied AI where sensing, actuation and interaction converge.

Our key research areas include:


1. Scalable, Efficient & Forgetful AI
2. Language, Generative & Multimodal Models

3. Graph, Streaming & Spatio-temporal Learning
4. Human-Centered, Ethical & Trustworthy AI

5. Vision, Signal & Multimedia Systems
6. ML for Human Behaviour, Health & Ubiquitous Systems

7. Distributed & Federated ML Systems

8. Cyber-Physical Systems & Embodied AI

Publications and Projects

Our Publications list provides details of our published papers in books, journals and conferences.

We are involved in many diverse research projects funded by several external bodies such as EPSRC, Alan Turing Institute, ARIA, ERC, MRC Royal Society, Innovate UK etc.

AI & ML Systems News

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PhD Applications

We welcome applications for PhD starting in October 2026.

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