Chris Barnes is Head of Science of AI at the National Physical Laboratory (NPL), where he leads strategic research into AI uncertainty quantification, trustworthy AI, and metrics for data quality. His work supports the development of reliable, transparent, and fit-for-purpose AI systems, particularly in critical sectors such as healthcare and manufacturing. He also holds a professorship in Systems and Synthetic Biology at University College London (UCL), where his academic research focuses on AI, computational modelling, engineering biology, and biomedical applications.

Chris earned both his MSci and PhD in Physics from Imperial College London, specialising in high-energy particle physics. His early postdoctoral work included roles at CERN, the Wellcome Trust Sanger Institute and Imperial College London, where he contributed to theoretical systems biology and genomics. He joined UCL in 2012 as a Wellcome Trust Research Career Development Fellow and was appointed Professor in 2019. He has also served as a consultant for a number of companies and charities.

Chris has authored over 100 peer-reviewed articles, with a Google Scholar h-index of 67 and more than 26,000 citations. His research spans statistics, machine learning, engineering biology, systems biology, and quantitative modelling. He was a Turing Fellow, and recipient of the UCL Provost Teaching Award. He has received funding from Wellcome, European Research Council, EPSRC, BBSRC, and Leverhulme Trust.

MultimodalAI'26 Keynote Title: An overview of AI at NPL

MultimodalAI'26 Keynote Abstract: The National Physical Laboratory (NPL) is the UK’s national metrology institute. It develops and maintains the country’s primary measurement standards and provides the measurement science, calibration, testing, and scientific expertise that underpin industry, innovation, and public services—helping ensure measurements are accurate, comparable, and trustworthy. In this talk, Chris Barnes (Head of Science for AI, NPL) will give an overview of NPL’s AI activity and how it supports the UK’s ambitions for trustworthy and deployable AI across industry.

The talk is structured into three parts covering the main aspects of AI@NPL. First, Trustworthy AI: how measurement thinking translates into AI practice, covering concepts such as uncertainty, robustness, interpretability, and data quality, and how these properties can be characterised, tested, and compared in a repeatable way. Second, AI for Metrology: examples of how modern machine learning can be used to enhance the scientific process. Third, AI assurance: the emerging methods, standards, and evaluation approaches needed to build justified confidence in AI systems for critical applications.