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A case for data-centric engineering – Professor Mark Girolami at The Royal Society

Turing Fellow Professor Mark Girolami took his data-centric engineering mission to The Royal Society last week. In his lecture, ‘Data-centric Engineering: Hype or Hope?” Professor Girolami used real world examples to emphasise the importance for ensuring mathematical sciences are at the core of today’s data-rich economies.

“Every day in what I do in my research and my interaction with industry and commerce there is absolutely no doubt that despite us being in such a data rich world, it’s a very uncertain one… [sic] we need to account for that uncertainty” – Professor Mark Girolami, FRSE

While there is a lot of hype behind this new perspective on engineering, Professor Girolami made it clear that he has a great deal of hope. His group at the Turing are now interacting with a variety of major commercial partners, including Siemens, Network Rail and the National Grid to address core engineering issues using computational statistics.

“In terms of mathematics for a modern economy, what we are doing here by supporting engineering, trying to address engineering companies, trying to address contemporary problems with foundational mathematics and foundational statistical science is a great hope.”

Professor Mark Girolami, Director for the Turing-Lloyd’s Register Foundation Data-Centric Engineering Programme.

For more information on the programme for data-centric engineering, please visit turing.ac.uk/data-centric-engineering/

You can watch the full video below, courtesy of The Royal Society.

Professor Mark Girolami FRSE is Director of the Lloyds Register Foundation Programme on data-centric engineering. Mark’s research covers the investigation and development of advanced novel statistical methodology driven by applications in the life, clinical, physical, chemical, engineering and ecological sciences. He also works closely with industry where he has several patents leading from his work on e.g. activity profiling in telecommunications networks and developing statistical techniques for the machine based identification of counterfeit currency.

This lecture was part of the Royal Society conference on Mathematics for the modern economy. The scientific meeting bridged the gap between the UK’s expertise in industrial mathematics and those who apply it, whether in industry, government or other academic disciplines.