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Interpretable and trustworthy situational understanding for multivariate time series in cyber-physical systems.
- Funding
- US EOARD
AI, cybersecurity and reasoning for decisions, developed through funded research and collaborations with universities, laboratories and industry.
Interpretable and trustworthy situational understanding for multivariate time series in cyber-physical systems.
Uncertainty-aware neuro-symbolic architectures for signal intelligence and complex event processing.
Spatial omics and integrated bioimaging research infrastructure, connecting biomedical research with AI-enabled analysis and scientific computing.
Neuro-symbolic active cyber defence for extracting actionable intelligence from logs and heterogeneous evidence.
Argumentation and causal modelling to investigate energy consumption in neural-network training and support informed decisions about efficiency and performance trade-offs.
AI, causality, and reasoning to improve the resilience of deceptive assets in cyber defence.
Effective collaboration between humans and AI agents in teams that must perceive, reason, communicate, and act.
Research on scientific enquiry under qualitative and quantitative uncertainty, funded through the Rita Levi-Montalcini programme.
Learning and reasoning in coalition information environments, with a consortium involving Cardiff, UCL, UCLA, IBM, ARL and Dstl.
AI and data mining for the analysis of international criminal trials, in a project coordinated by Swansea University.
Human–machine collaboration for intelligence analysis, coordinated by Southampton with Cardiff and St Andrews.
Research infrastructure for interaction and modelling of action, control and tracking at Cardiff University.
Research on migration and refugee policy-making in the presence of rumours and unsubstantiated claims, collaborating with Özyeğin University.
Inference management for information sharing within coalitions.
Research on interpreting incomplete, uncertain and changing data to support decision-making.
Information fusion and inference management under uncertainty.