Chris is a Senior Expert advisor on the use of AI and ML to accelerate scientific discovery (Scientific AI). He advises leading biopharma clients on the safe adoption of state of the art AI for drug discovery and development, including the use of AI on Knowledge Graphs, Causal Machine Learning, and large Foundational Models.
He operates as a trusted advisor of C-level executives and Heads of Research and Development, as well as a leader of large technical teams delivering technology.
He acts as the global lead on AI Methodological Innovation for Biopharma R&D, and expert on Responsible AI across domains, including Generative AI safety.
Chris is a Chartered Statistician with the Royal Statistical Society and an Honorary
Associate Professor at Imperial College, where he has designed and delivered the
first-of-its kind postgraduate programme in Ethical AI. He is regularly invited to give
keynote presentations on topics related to AI in the Life Sciences and AI Safety.
He is widely published in AI/ML methodology for life sciences, such as rare disease and functional MRI analysis.
Following his PhD in statistical machine learning theory, and a prestigious Research Fellowship at the University of Cambridge, Chris became a tenured member of staff at Imperial College for several years, leading independent research in explainable AI. Prior to joining McKinsey, he founded and led as Chief Scientist a startup in statistical machine
learning and served as the Head of Research of London-based tech unicorn Improbable working on AI methods for high fidelity modelling and simulation in digital twins, resulting in groundbreaking acceleration of modelling and simulation also featured in Wired magazine.
BA in Mathematics, Cambridge Uni MSc in AI from Edinburgh Uni. MSc in Logic & Algorithms, Athens Uni. PhD in Statistics from Imperial College Several CPD courses in the life sciences