Dr Vasileios Charitopoulos
Lecturer in Product & Processing Systems Engineer
Dept of Chemical Engineering
Faculty of Engineering Science
- Joined UCL
- 1st Sep 2015
My research focuses on the development of computational frameworks to aid decision-making in multi-scale problems under uncertainty with foundations on numerical optimisation, applied statistics and process systems engineering. Key application domains involve:
- Industry 4.0 & Cybermanufacturing systems engineering
- Hydrogen economy and energy carriers modelling & optimisation
- Industrial decarbonisation strategies
- Decision-making under uncertainty: Theory & algorithms
My teaching interests lie on the interface of mathematics, computer-aided chemical engineering and process control. I am a passionate advocate of research-based education and blended learning practices as means of engaging students and ensuring their success in understanding and solving real-world problems.
Currently, I am involved in teaching of the following modules:
Dr. Vassilis Charitopoulos is a Lecturer (Assist. Professor) in the Department of Chemical Engineering, CPSE at UCL. He specialises in developing mathematical programming models and methods to incorporate uncertainty considerations for chemical process and energy systems engineering problems. He holds a Diploma in Chemical Engineering from the National Technical University of Athens and a PhD from the Department of Chemical Engineering at UCL.
Before joining UCL, he was a Research Associate at the Energy Policy Research Group at Cambridge Judge Business School where he worked on optimisation of heat decarbonisation pathways. Dr. Charitopoulos has received global recognition for his excellent research activity including: Springer Thesis Award (2019), UCL David Newton Prize (2019). He was the recipient of an Early Career Fellowship (2019) from the Isaac Newton Trust, University of Cambridge. In 2020, he was Highly Commended as Best Young Researcher by the IChemE Global Awards.
His current research focuses on the development of novel techniques for model-based and data-driven optimisation frameworks for digital process manufacturing and sustainable energy systems engineering.