Teaching Philosophy
The teaching programme is grounded in the belief that rigorous mathematical foundations, combined with practical computational experience, prepare students to tackle complex real-world problems. Modules integrate theoretical frameworks from computational intelligence with hands-on implementation, ensuring that students develop both analytical and practical skills.
Teaching has been delivered across multiple institutions, including the Nottingham Trent University, the University of Nottingham (UK and Malaysia campuses), and through visiting lectureships at universities in Japan, Finland, Italy, Canada, and New Zealand.
University Modules
Over the course of the academic career, the following areas have formed the basis of taught modules at undergraduate and postgraduate level:
Computational Intelligence
An advanced module covering fuzzy logic, neural networks, evolutionary computation, and their integration. Students explore the theoretical underpinnings and implement solutions using computational intelligence techniques for classification, optimisation, and decision-making.
Systems Modelling and Simulation
Covering discrete-event simulation, continuous simulation, and hybrid approaches. The module equips students with methodologies for building, validating, and analysing simulation models of complex systems drawn from engineering and operational contexts.
Software Engineering
A foundation module in software design principles, development methodologies, and quality assurance. Topics include requirements analysis, design patterns, testing strategies, and project management within collaborative development environments.
Parallel and Distributed Computing
Examining architectures, algorithms, and programming models for concurrent computation. Students gain experience with message-passing, shared memory models, and practical implementations on cluster and cloud platforms.
Information Processing and Uncertainty
An exploration of how uncertainty pervades information systems and the mathematical tools available for managing it. Topics range from probability and fuzzy set theory to rough sets and granular computing, applied to real-world data interpretation challenges.
Database and Information Systems
Covering the design, implementation, and querying of relational and non-relational databases. The module addresses data modelling, normalisation, SQL, and the integration of database systems with application logic.
Research Supervision
The supervision of doctoral research is a central component of the academic programme. Over 50 PhD and MPhil candidates have completed their research under supervision, contributing to fields including granular computing, neural computation, image processing, scheduling and timetabling, traffic information systems, and distributed computing. A full list of research students and their topics is available on the People page.
The supervision approach emphasises independence of thought, methodological rigour, and the development of transferable skills in analysis, communication, and critical evaluation. Students are encouraged to present their work at international conferences and to publish in peer-reviewed journals throughout their research programme.