Introduction
The goal of the programme was to make data science, mathematical and statistical techniques fundamental to engineering practice, making engineering safer and smarter and leading to a world in which all that is engineered – buildings, transport, energy systems, and much more – is more intelligently designed, built and maintained, more energy efficient and ultimately safer to use and live in.
The programme was structured around three over-arching ‘Grand Challenges’ designed to meet the data-centric engineering needs of society and industry:
- Resilient and robust infrastructure – This grand challenge focused on work relating to major infrastructure systems such as railways, power plants, and supply chains that are vital to our way of life. Projects under this grand challenge contributed to operating more resilient infrastructure systems, robust against shocks or unexpected incidents and preventing future ‘black sky’ events.
- Monitoring of complex systems – These projects worked to improve the operation of old and new infrastructure, allowing them to be used in smarter and safer ways to make the best use of data to preserve the operation of vital systems. This grand challenge contained projects focusing on array of infrastructure including railway bridges, gas turbines, transport systems, and even cyber networks.
- Data-driven design under uncertainty – This grand challenge aimed to address the fundamental questions of optimal data collection and design optimisation in uncertain environments upon the increasing use of sensors and monitoring systems being employed in engineering applications.
Read more about the first phase of the programme – data-centric engineering at the Turing: the story so far and the first ever data-centric engineering summit – DCEng Summit
DCE 1.0 Impact
Strategic Leads
Professor Omar Matar
Data-Centric Engineering Strategic Leader, The Alan Turing Institute & Vice-Dean (Education), Faculty of Engineering, Imperial College LondonProfessor Julie A McCann
Data-Centric Engineering Strategic LeaderProfessor Emma McCoy
Data-Centric Engineering Strategic LeaderProfessor John Moriarty
Professor of Mathematics, Queen Mary University of LondonProfessor Jennifer Whyte
Data-Centric Engineering Strategic LeaderGroup leaders
Dr François-Xavier Briol
Data-Centric Engineering Group LeaderDr Ruchi Choudhary
Data-Centric Engineering Group LeaderDr Theo Damoulas
Turing AI Acceleration FellowDr Andrew Duncan
Director of Science & Innovation - Fundamental Research. Department/Programme: Fundamental Research, Programme LeadershipProfessor Serge Guillas
Data-Centric Engineering Group LeaderProfessor Adam Johansen
Data-Centric Engineering Group LeaderDr Luca Magri
Group Leader, Physics-informed machine learning and data assimilationDr Nikolaos Nikitas
Data-Centric Engineering Group LeaderDr Indranil Pan
Data-Centric Engineering Group LeaderDr Pranay Seshadri
Data-Centric Engineering Group LeaderProfessor Adam Sobey
Programme Director, Data-Centric EngineeringDr Gabe Weymouth
Data-Centric Engineering Group LeaderDr Craig Buchanan
Data-Centric Engineering Group LeaderDr Weisi Guo
Honorary Professor at University of Warwick & Professor of Human Machine Intelligence at Cranfield UniversityDr Steven Reece
Senior Research Associate, Machine Learning Research Group, University of OxfordDr Victoria Stephenson
Data-Centric Engineering Group LeaderDr Myriam Neaimeh
Data-Centric Engineering Group LeaderOrganisers
Katy Henderson
Programme Manager, Data-Centric EngineeringAnastasia Shteyn
Programme Manager, Data-Centric EngineeringProfessor Mark Girolami
Chief ScientistAlice Budden
Research Project ManagerMark Saunders
Partnerships Development LeadJournal
The programme has partnered with Cambridge University Press on the launch of a new open access journal, Data-Centric Engineering.
The vision for the journal is to publish high quality research using data-intensive approaches in any of the engineering sciences so that emerging ideas can be accelerated in research and practice. The journal can be read, redistributed, and re-used without barriers. Importantly, this includes those with a stake in these developments outside of academia who usually do not have access to academic publications, such as those in industry and policy fields.
International connections
To have greatest impact, the programme has always recognised that data-centric engineering has to be adopted on a global basis. From the foundational community established at the Turing, the programme has worked to develop international connections to drive forward data-centric activity within engineering across the world.
Memoranda of understanding have been signed on three continents so far: with the Canadian Statistical Sciences Institute, the Oden Institute (The University of Texas at Austin), the Finnish Centre for Artificial Intelligence, and The University of Sydney. These agreements formally recognise shared ambitions around embracing data-centric methods in engineering and encourage the co-development of activities, working together on research, sharing knowledge, and hosting exchange visitors and events.
The programme has also developed collaborative international projects with partners based in Amsterdam, San Francisco and Singapore.
Contact info
For more information, please contact Alice Budden, Research Project Manager, [email protected]