Optimisation & intelligent control
Model predictive control, metaheuristics, and hybrid methods for nonlinear, constrained, and multi-objective systems — robotics, scheduling, and resource allocation under uncertainty.
Focus areas
Research, scholarship, and external engagement
Advanced control strategies, optimisation, machine learning, and AI — designed for rigorous implementation in real-world engineering contexts.
Research
My scholarly activity is interdisciplinary and practice-oriented, spanning AI, machine learning, control and automation, database systems, and applied digital technologies. I aim to ensure that research and external engagement inform teaching, curriculum design, and student learning — with advanced control strategies, optimisation, and responsible AI practice grounded in real-world engineering contexts.
Applied machine learning & LLMs
Deep learning for forecasting and anomaly detection; language-model approaches for structured triage and decision support, with emphasis on validation and governance.
Energy, tariffs & infrastructure AI
Microgrids and renewable integration; optimisation of energy prices and tariffs; smart energy modelling linking simulation, control, and learning.
Trustworthy & secure computing
Security-relevant ML, ethical AI assessment as a global IEEE ethics assessor (IEEE Global AI Ethics Assessor programme), and governance-aware design for high-stakes environments.