Machine Learning · Optimisation · AI for Science
Data-driven models, advanced control, and AI-assisted discovery for processes and physical systems.
Our Research
We are the LOOPS research group — Learning and Optimisation Of Process Systems — based in the Department of Chemical Engineering at UCL, and part of the Sargent Centre for Process Systems Engineering. We develop machine learning, optimisation and control methods for processes and physical systems. Current projects include hierarchical MPC–RL control, LLM-guided symbolic regression, physics-based modelling for froth flotation control, stochastic MPC, and more.
Physics-informed ML, hybrid models, Gaussian processes, Bayesian optimisation, reinforcement learning and explainable AI.
Model predictive control, economic MPC, real-time optimisation, digital twins and autonomous process operation.
AI-assisted discovery, materials informatics, optimal experimental design and closed-loop experimentation.
