Menu Bringing together world-leading academic institutions and major industrial partners from across the engineering sector, to address new challenges in data-centric engineering Learn more Introduction The next decade will see step changes in data-driven technology, impacting all aspects of engineering and industry. In preparation, the Lloyd’s Register Foundation and The Alan Turing Institute have partnered on a major initiative to address new challenges in data-centric engineering. Based at The Alan Turing Institute, the programme brings together world-leading academic institutions and major industrial partners from across the engineering sector. The goal of the programme is 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 for data-centric engineering is led by Professor Mark Girolami. Programme challenges The programme is structured around three over-arching ‘Grand Challenges’ designed to meet the data-centric engineering needs of society and industry: Resilient and robust infrastructure Monitoring of complex systems Data-driven design under uncertainty These challenges have been selected to enable the programme to provide the greatest societal impact. The programme has initiated a range of projects focused on delivering the path to achieving the Grand Challenges. Learn more about the programme’s projects Impact Using machine learning to improve the reliability of wireless communication systems A collaboration involving the Turing has developed a new method for improving wireless technologies such as Wi-Fi. Optimising the world’s first underground farm In the heart of London there is a farm like no other. It’s subterranean, sustainable and energy smart. It also has a digital twin looking out for its every need. ­­­­World first as 3D printed steel mart bridge opens to pedestrians in Amsterdam The world’s first 3D printed steel bridge opens to the public today: a pedestrian structure located in the city centre of Amsterdam. The bridge is a result of a multi-disciplinary team of experts collaborating on the future design of public spaces. Read more about the real world impact of the data-centric engineering programme Journal 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. Read more about the Data-Centric Engineering journal 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. Sign up to our mailing list Organisers Professor Mark Girolami Chief Scientist Anastasia Shteyn Programme Manager, Data-Centric Engineering Katy Henderson Research Project Manager, Data-Centric Engineering Contact info For more information, please contact Katy Henderson, Research Project Manager, dceprogramme@turing.ac.uk Jump To Introduction Challenges Impact Journal People Organisers Contact info Funders Programme articles Programme projects Programme publications Programme challenges Impact Funders Programme articles Using machine learning to improve the reliability of wireless communication systems A collaboration involving the Turing has developed a new method for improving wireless technologies such as Wi-Fi Wednesday 15 Sep 2021 Optimising the world’s first underground farm In the heart of London there is a farm like no other. It’s subterranean, sustainable and energy smart. It also has a digital twin looking out for its every need World first as 3D printed steel smart bridge opens to pedestrians in Amsterdam The Turing's data-centric engineering programme has been integral to the bridge’s development Thursday 15 Jul 2021 Why we created a ‘digital twin’ of the world’s first 3D printed steel bridge As the bridge opens to the public in Amsterdam, the Turing’s Eric Daub explains the thinking behind our digital version Thursday 15 Jul 2021 Bridging the gap between physical and digital The Turing’s data-centric engineering programme and its collaborators are unlocking insights into the world-first 3D printed steel bridge, using innovative data science techniques and ‘digital twin’ technology Helping London to navigate lockdown safely Project Odysseus monitors activity on the streets of London, allowing authorities to make interventions to keep people socially distanced Pagination Current page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Next page Next › Last page Last » Programme events Standards-Based Approaches for Distributed Energy Resources Thursday 03 Dec 2020 Time: 14:30 - 17:30 Ben Godfrey Farina Farrier Rolf Bienert Walt Johnson, U.S. Electric Power Research Institute (Ret.) Bob Andrews, CEO, Zevtron Ben Aylott, Carbon Co-op Dr. Nina Klein Smart Charging Thursday 19 Nov 2020 Time: 15:00 - 16:00 Gerhard Paris Maren Fiege Modern cryptography in the context of electric vehicles Wednesday 11 Nov 2020 Time: 16:00 - 17:00 Nadim Kobeissi Webinar: Edge computing for Earth observation Tuesday 17 Mar 2020 Time: 10:00 - 18:00 Daniel Andre Kin K. Leung Michael Woollard Noa Zilberman Phillip Stanley-Marbell Pui Anantrasirichai Richard Walters Rob Mullins Sam Doody Tim Wright Victoria Nockles AI to advance wellbeing and society Tuesday 06 Aug 2019 Time: 14:30 - 16:30 Victor O.K. Li CogX 2019 Monday 10 Jun 2019 - Wednesday 12 Jun 2019 Time: 10:00 - 16:00 Adrian Smith Maria Liakata Helen Margetts Adrian Weller Maria Wolters Lukasz Szpruch Omar A Guerrero Daphne Ezer Mark Briers Cosmina Dorobantu Andrew Duncan Theo Damoulas Mark Freestone Miguel Rodrigues Sethu Vijayakumar David Llewellyn David Leslie Bertie Vidgen David De Roure Alastair Gregory Guillem Mosquera Jon Crowcroft Mariarosaria Taddeo Ganna Pogrebna Indranil Pan Pagination Current page 1 Page 2 Page 3 Next page Next › Last page Last » Programme projects Future Indonesian Tsunamis: Towards End-to-end Risk quantification (FITTER) Using high performance computing, uncertainty quantification, and economics to evaluate the risk posed to livelihoods by future tsunamis in Indonesia Adaptive multilevel MCMC sampling Developing an MCMC algorithm for efficient Bayesian inference in multilevel models Uncertainty quantification for black box models Developing methods for the quantification of uncertainty in complex numerical models Project Odysseus – Understanding London ‘busyness’ and exiting lockdown Capturing activity over London to better understand 'busyness' and aid effective policy-making strategies for exiting the pandemic lockdown Digital twins for multiphase flow systems Integrating data, physics and machine learning to develop rapid predictive models for engineered multiphase flow systems AI for control problems Using a competition platform to accelerate progress in data-driven control problems Pagination Current page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7 Page 8 Page 9 … Next page Next › Last page Last » Programme publications Stein Thinning Optimal Thinning of MCMC Output The use of heuristics to assess the convergence and compress the output of Markov... Riabiz M, Chen WY, Cockayne J, Swietach P, Niederer SA, Mackey L, Oates CJ. (2020) Optimal Thinning of MCMC Output. arXiv:2005.03952. COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing The rapid global spread of COVID-19 has led to an unprecedented demand for effective... Gupta, Prateek, Tegan Maharaj, Martin Weiss, Nasim Rahaman, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan et al. "COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing." arXiv preprint arXiv:2010.16004 (2020). Near Real-Time Social Distancing in London During the COVID-19 pandemic, policy makers at the Greater London Authority, the regional governance... Near Real-Time Social Distancing in London, James Walsh, Oluwafunmilola Kesa, Andrew Wang, Mihai Ilas, Patrick O'Hara, Neil Dhir, Theodoros Damoulas, arXiv:2012.07751. Project Odysseus Won CIUK Student Poster Competition 2020 Patrick O'Hara, James Walsh, Chance Haycock, Edward Thorpe-Woords, Oluwafunmilola Kesa, Andrew Wang, Mihai Ilas, James Brandreth, Oscar Giles, Neil Dhir, Theodoros Damoulas, Computing Insight UK 2020. Generalized Bayesian filtering via sequential Monte Carlo We introduce a framework for inference in general state-space hidden Markov models (HMMs) under... A. Boustati, Ö. D. Akylidìz, T. Damoulas, and A. M. Johansen (2020). Generalized Bayesian filtering via sequential Monte Carlo. In Advances in Neural Information Processing Systems 33 (NeurIPS). To appear. 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