Sam Kay

Sam is a PhD researcher in Chemical Engineering at the University of Manchester, supervised by Dr Dongda Zhang. His research lies at the intersection of machine learning, data analytics and process systems engineering, with a focus on developing intelligent digital tools for process design, optimisation and control.

His PhD research focuses on data-driven and hybrid modelling approaches for the design, control, and scale-up of complex formulated products. This includes the development of automated frameworks for process flow diagram generation, predictive digital twins, and flexible and optimal operational-space identification under problematic industrial uncertainties. More broadly, his work explores how machine learning, mechanistic modelling and optimisation can be combined to enable more flexible, efficient and sustainable manufacturing processes.

Sam’s other research has included industrial soft-sensor development, transfer learning, interpretable data-driven modelling and uncertainty-aware process optimisation, with several publications developed through both academic and industrial collaboration, including partners such as Unilever and CPACT (The Centre for Process Analytics and Control Technology).

Sam was awarded an SCI Scholarship in 2025 to support his doctoral research, and has presented his research at many international and domestic conferences.