LOOPS

Learning & Online Optimization for Processes and Systems

UCL

We are a research group in the Department of Chemical Engineering at UCL, led by Dr Paulina Quintanilla, and part of the Sargent Centre for Process Systems Engineering. We develop and apply machine learning, optimisation and control methods to processes and systems with a sustainability focus, spanning three connected areas.

LOOPS research areas: Machine Learning & AI, Optimisation & Control, AI for Science & Materials

Machine Learning & AI

Data-driven models that learn, adapt and improve to make better predictions and decisions. We build models directly from process data and combine them with physics-based knowledge to capture complex, nonlinear behaviour where first-principles models alone fall short.

Optimisation & Control

Optimisation and control in the loop to operate systems efficiently, safely and sustainably. We design closed-loop optimisation and advanced control strategies — from model predictive control to real-time optimisation — that keep processes running at their best despite uncertainty and disturbances.

AI for Science & Materials

AI and data-driven discovery to accelerate scientific understanding and materials innovation. We combine experiments, predictive models and screening tools in a discovery loop to speed up the development of new materials and processes.

If you are interested in joining or working with our group, please visit our Team page or email p.quintanilla@ucl.ac.uk. It would be helpful if you could attach your CV, links to your LinkedIn or Google Scholar profiles (if you have them).

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