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Artificial intelligence

Multi-Agent Systems
Developing trust, reputation, normative and biologically inspired techniques for establishing, managing and influencing cooperation while coping with sparsity of information, malicious agents, lack of reciprocity and dynamism in large-scale distributed systems.
Strategic Artificial Intelligence
Use of game-theoretic techniques for constructing rational agents and the regulation of online interaction (e.g., matching, fair division, manipulation avoidance), including mechanism design.
Explainable AI
Investigating techniques to capture the context behind a given action or decision by an AI system, and generate human-directed rationale and explanation.

Postgraduate Research


We invite applications from well qualified, enthusiastic students to join our research community.
MSc and PhD Applications

Our research students benefit from world-leading staff and facilities.
Current Research Students
Multi-Agent Reinforcement Learning
Design and analysis of reinforcement learning algorithms with tight performance guarantees, and investigation of multi-agent reinforcement algorithms.
Intelligent Cyber-Physical Systems
Developing novel sensing, learning and actuation approaches for intelligent cyber-physical systems, and investigating on-device AI to run efficiently on mobile devices, wearables, and IoT.
Deep Learning for Imaging Data
Developing coding methods for images and video for efficient compression and minimum visual distortion, and developing privacy preserving deep learning models for imaging data.
Intelligent Connected Vehicles
Developing AI techniques for connected vehicles, including intelligent data compression, occupant monitoring, pattern-of-life prediction and investigating the psychological aspects of human-autonomous vehicle interactions.