Biography
Professor Mark Girolami is the Academic Director for CDBB. In this role he provides academic leadership for CDBB across the University and more broadly throughout the national and international research communities.
Mark Girolami joins CDBB from the Department of Mathematics at Imperial College London, where he holds both the Chair of Statistics and the Lloyds Register Foundation-Royal Academy of Engineering Research Chair in Data Centric Engineering.
He was one of the original founding Executive Directors of the Alan Turing Institute (Turing), the UK’s national institute for Data Science and Artificial Intelligence, after which he was appointed as Strategic Director at Turing, where he established and continues to lead the Programme on Data Centric Engineering.
Mark is an elected fellow of the Royal Society of Edinburgh, he was an EPSRC Advanced Research Fellow (2007-2012), an EPSRC Established Career Research Fellow (2012-2018), and a recipient of a Royal Society Wolfson Research Merit Award.
Prior to embarking on an academic career, he spent a decade with IBM as a Chartered Mechanical Engineer working in diverse areas such as high volume manufacturing automation and the exploitation of computational fluid dynamics in electronic systems design.
Mark is recognised for his research contributions that sit at the interface of the Statistical, Mathematical, Physical, Life, and Engineering Sciences. The impact of his research includes successful translation to new products and services for companies and organisations such as, for example, National Cash Registers and Amazon, as well as providing essential data modelling and analysis tools employed the world over by neuroscientists and cellular biologists.
His recent work introducing Riemann Manifold Langevin & Hamiltonian Monte Carlo methods for Statistical Inference was Read before the Royal Statistical Society, receiving the largest number of contributed discussions of a paper in its 183-year history. These Monte Carlo methods are now enabling the quantification of uncertainty in systems and phenomena as diverse as, for example, whole heart models, turbulent combustion, deep learning for artificial intelligence, virus transmission, new material characterisation, and structural health monitoring.
