Rate My Professor Maryam Farsi

Dr Maryam Farsi is a Senior Lecturer in Engineering Optimization at Cranfield University’s Faculty of Engineering and Applied Sciences. She obtained her PhD in Nonlinear Structural Mechanics from Imperial College London in December 2014 and her MSc in Civil Engineering Structures from City, University of London in March 2010. Dr Farsi leads the Complex Systems and Optimization research group at the Centre for Digital Engineering and Manufacturing. Her primary research vision spans Systems Engineering, Net-Zero Engineering, and Cost Engineering, with expertise applied in design, aerospace, manufacturing, transport, and healthcare. She focuses on the development of innovative autonomous systems and system-of-systems optimization, the decarbonization of engineering assets and manufacturing processes through optimized Net-Zero strategies, and the application of artificial intelligence and digital twin technologies in whole-life cost analysis. Her research is mainly quantitative, emphasizing multi-objective optimization to trade-off between economic, environmental, and social sustainability.

Dr Farsi is a member of the Institution of Engineering and Technology, a Fellow of the Higher Education Academy, a committee member of the Society for Cost Analysis and Forecasting, a member of the Community of Practice for the Association of Cost Engineers, and an associate editor of the International Journal of Strategic Engineering. She has served as Project Lead for the EPSRC IAA ECR project on Cost of Energy in Net-Zero and Decarbonisation and as Project co-Lead for the EPSRC Transport Decarbonisation TransiT project. Dr Farsi has authored or co-authored numerous publications including papers in the Journal of Manufacturing Systems, Reliability Engineering & System Safety, and the International Journal of Production Economics, as well as contributions to books on digital twin technologies and sustainable aviation. She currently supervises multiple PhD researchers working on topics such as digital twins for predictive maintenance, AI-based cost estimation for net-zero transitions, and optimisation of through-life engineering services.