Systems Engineering and Complexity Research Group

Our research and associated teaching explores the development and application of systems engineering and systems approaches to solve complex problems and engineer solutions for a better world.

About us

We work on various cross-disciplinary projects in several domains, from hard systems engineering to socio-technical and enterprise systems. We develop advanced modelling, co-simulation, interactive visualization techniques, model-based systems engineering, big data and digital twins to gain a greater understanding of the performance, behaviour and emergent properties of complex systems and associated advanced technology across a wide range of application areas.

We research how new systems approaches can support policy development and better determine the impact of policy in complex and sensitive areas such as energy, food, climate and environment, and the preservation of strategic national resources and the Circular Economy.

We explore digital and virtual engineering practices and data-intensive applications in the connected environment of data, people, processes, services, systems and production assets.

Our strength is in the breadth of our industrial collaboration and our group includes, visiting professors and fellows based in industry. We have carried our research with BAE Systems, Airbus, Rolls Royce, Thales, QinetiQ, Severn Trent Water, Nuclear Waste Services, UK MoD, NATO, BGL Group, IBM, JLR, Lloyds, PWC, SCOR, WTW, Z/Yen
Group, Baxi and Porterbrook to name a few.

Our group currently supervises 19 PhD students and participates in activities with other research groups, such as the Intelligent Automation Research Group and the Advanced VR Research Centre.

Our expertise

Our research expertise includes, but is not limited to, the following:

  • Methods, languages and tools for Model-Based systems engineering
  • The mathematical and scientific foundation of architecture and systems engineering
  • Design methodologies for human-centric digital technologies and systems
  • Data analysis of big data and unstructured data from citizen science projects
  • Cyber-Physical Systems of Systems Engineering
  • Using formal ontologies, transformer-based models and agent-based learning
  • Wireless network stack optimisation and IoT/AI-based process monitoring and decision-making
  • Digitalisation, Engineering Information-Intensive Ecosystems, Systemic Design and Trans-disciplinary Engineering, Systems Validation and ‘what works’ Evaluation

Charles Dickerson

Melanie King

Mey Goh

Michael Henshaw