We develop machine learning methods that enable engineering systems to learn from data, improve predictions, and support decision-making under uncertainty. Our research combines statistical learning, probabilistic modelling, and physics-informed AI to create models that are both accurate and scientifically meaningful.

A particular focus is the integration of domain knowledge into machine learning, ensuring that AI systems remain interpretable, robust, and reliable when applied to complex real-world problems.

Research topics: Physics-informed machine learning · Hybrid modelling and digital twins · Gaussian processes · Scientific machine learning · Bayesian optimisation · Reinforcement learning · Explainable AI · AI-assisted model discovery

We develop optimisation and control methods for complex engineering systems operating in dynamic and uncertain environments. Our goal is to design intelligent decision-making frameworks that improve efficiency, safety, sustainability, and economic performance.

Our research spans real-time optimisation, model predictive control, stochastic optimisation, and digital twins. We combine mathematical models, machine learning, and feedback control to create closed-loop systems that adapt to changing conditions.

Research topics: Model predictive control (MPC) · Economic and stochastic MPC · Real-time optimisation · Dynamic modelling · Digital twins · State estimation · Decision-making under uncertainty · Autonomous process operation

We use artificial intelligence and optimisation to accelerate scientific discovery and the development of sustainable technologies. We create closed-loop discovery frameworks that can rapidly identify promising materials, processes, and operating conditions.

Our vision is to move beyond using AI solely for prediction and towards AI systems that actively support scientific reasoning, hypothesis generation, experiment design, and knowledge discovery.

Research topics: AI for Science · Autonomous discovery systems · Materials informatics · Scientific knowledge extraction · Optimal experimental design · Closed-loop experimentation · Scientific foundation models · Sustainable materials and technologies