DrMichel-Alexandre Cardin
Associate Professor in Engineering Systems Design
Dyson School of Design Engineering - Faculty of Engineering
Orcid identifier0000-0002-2337-1133 (opens in a new tab)
- Associate Professor in Engineering Systems DesignDyson School of Design Engineering - Faculty of Engineering
- 020 7594 1893 (Work)
- Royal College of Science Observatory Building, Room 1M03, Dyson Building, South Kensington Campus, United Kingdom
RESEARCH
His expertise lies in the translation of principles from real options theory into impactful tools and methodologies to support engineering systems design practice. His work focuses on development and evaluation of new computational techniques, digital processes, and algorithms to support the design of complex engineered systems like energy, transportation, and space infrastructure systems. It involves methodological topics such as concept generation and selection, decision-making under uncertainty, machine learning, stochastic optimization, uncertainty modeling, and virtual/augmented reality.
An important theme in the research is the concept of Flexibility in Engineering Design. This paradigm emerged from the theory of Real Options and seeks to design complex systems that adapt, reconfigure and change in the face of uncertainty and risks, with the goal of improving economic performance, sustainability, and resilience. Research on this emerging paradigm is highly needed, especially given ongoing threats from climate change, healthcare emergencies, geo-political tensions, and cyber-physical terrorism. Latest efforts focus on the development of a theory for Data-Driven Systems Design for Uncertainty, which explores the roles of AI and machine learning as part of this emerging paradigm.
An important theme in the research is the concept of Flexibility in Engineering Design. This paradigm emerged from the theory of Real Options and seeks to design complex systems that adapt, reconfigure and change in the face of uncertainty and risks, with the goal of improving economic performance, sustainability, and resilience. Research on this emerging paradigm is highly needed, especially given ongoing threats from climate change, healthcare emergencies, geo-political tensions, and cyber-physical terrorism. Latest efforts focus on the development of a theory for Data-Driven Systems Design for Uncertainty, which explores the roles of AI and machine learning as part of this emerging paradigm.