Dr Agrell leverages extensive experience within developing trustworthy AI, particularly for high-risk and safety-critical systems in an industrial context. He is driven by a passion for the intersection of machine learning, uncertainty quantification, physics based and data-driven simulation, assurance of complex systems and risk.
His focus is on developing trustworthy AI for high-risk and safety-critical systems, working at the intersection of machine learning, uncertainty quantification, physics-based and data-driven simulation, assurance of complex systems, and risk. His work aims to revolutionize the ability of organizations with high-risk infrastructure, such as those in the energy sector, to adopt AI in a safe and reliable manner, enabling increased innovation and significant efficiencies.
His key expertise includes probabilistic machine learning, a type of machine learning developed for applications where uncertainty is important. He also has extensive experience in developing solutions for safety-critical applications, where combining data-driven modeling with domain-specific knowledge is key. Additionally, his key qualifications include uncertainty quantification, innovation, and the development of codes, standards and best practices.
I am passionate about supporting industries in developing trustworthy AI for high-risk and safety-critical systems. This ensures that organizations with high-risk infrastructure, like those in the energy sector, can safely and reliably adopt AI, driving innovation and efficiency.
He is active in the development of tools and methods for compliance with AI regulation, like the EU AI Act.
Experience from a range of projects within the energy and maritime sectors
Technical lead on machine learning and data science
Technical lead on risk and reliability analysis
Christian is active in the scientific community, focusing his research on topics such as: Probabilistic Machine Learning, Reinforcement Learning, Deep learning, Bayesian networks, Generative AI, Digital Twins, Surrogate modelling, Uncertainty Quantification, Gaussian Processes, Stochastic analysis.
See full overview of papers and publications on Google Scholar.
DNV
Senior Engineer
Specialist on risk, reliability and data science in projects for customers in the energy sector
Software developer for DNV’s risk management software applications
Developed design codes and best practices for offshore pipelines, in collaboration with industry partners through joint industry projects (JIPs). 65 % of all offshore pipelines worldwide are designed according to these
