Distributed and high-performance systems

This research group will provide theoretical and practical innovations in data intensive distributed systems (including clouds, HPC and quantum data centres), distributed machine learning models and self-adapting and physics informed digital twins to emulate the real time behaviour of (bio)mechanical and cyber-physical systems. Another key focus of the group will be to provide technical leadership in investigating state of the art high-performance computational infrastructures for artificial intelligence models and applications that exploit the emerging approaches in parallel and distributed systems such as exa-scale, quantum computing and edge enhanced system architectures to provide scalable, efficient and robust AI systems. The group will also investigate distributed models for data representation, and integration such as graphs, multi-agents and semantic models to offer reliable, consistent and networked systems.

The group conducts multidisciplinary research in machine learning, artificial intelligence (AI), and high-performance solutions for their deployment using:

Digital twins

• Self-learning digital twins for cyber-physical systems
• Self-adapting networks of digital twins
• Diffusion and assimilation models for high performance digital twins that combine physics-driven causality with simulations and observed data, enabling real-time predictions
• Physics informed neural networks to enable safe and robust digital twins that comply with mathematical guarantees such as partial differential equations

Distributed AI

• Distributed and high performance machine learning architectures for scale, performance and quality of service
• Embarrassingly parallel and spatially distributed AI models to exploit large scale computation for real-time predictions (including GPUs, FPGAs, HPC, Clouds)
• Edge enhanced, embedded and miniaturized systems to support locality, resource and performance aware machine learning
• Data intensive distributed systems for high performance inferencing

Knowledge and data engineering

• Scalable and distributed data management architectures for robust and elastic data storage
• Consistency, availability and partition tolerance (CAP) compliant models for data integration, modelling and data retrieval (No SQL, graph, replicated databases)
• High performance indexing, data modelling and knowledge engineering (warehousing, graphs databases, ontologies, semantics)
• Federated and privacy preserving data storage architectures (catalogs, blockchains, data lakes)

Research expertise

• Artificial Intelligence technologies
• Databases
• ICT networks and distributed systems
• Quantum devices
• components and systems

Group members

• Professor Ashiq Anjum (group lead)
• Dr John Drake
• Dr Rob Free
• Dr Xiao Chen
• Dr Wentao Li
• Dr Furqan Aziz
• Dr Genovefa Kefalidou