Projects
Real-time data assimilation for digital twins
Developing methods to improve the use of real-time data in system representations
Digital twins for multiphase flow systems
Integrating data, physics and machine learning to develop rapid predictive models for engineered multiphase flow systems
Intelligent virtual test pyramids for high value manufacturing
Developing novel AI methods to build a more sustainable aviation industry
Posterior bootstrap: A scalable approach to Bayesian non-parametric learning
Providing an open-source tool to make non-parametric statistical inference faster and more accurate
Automating data visualisation
Researching algorithms for visualising data in digital twins, including automating the layout of large scale data and visualising the uncertainty of data values
Data science tools for high-performance computing
Enabling the provision of popular data science packages within a managed, multi-user, academic high performance computing (HPC) environment
People
Professor Jon Crowcroft
Researcher at Large
Khalid Ayed Alharthi
Enrichment Student
Katharina Zuhlsdorff
Enrichment Student
Dr Emily Lines
Turing Fellow
Dr Grigorios Mingas
Senior Research Data Scientist
Dr Gabe Weymouth
Data-Centric Engineering Group Leader
Szymon Walkowiak
Doctoral Student
Dr Oscar Giles
Research Data Scientist
Professor David De Roure
Turing FellowEvents
Reliability and reproducibility in computational science
Friday 24 Jan 2020
Time: 09:30 - 17:00
Introduction to Spark for Data Scientists
Thursday 31 Oct 2019 - Friday 01 Nov 2019
Time: 10:00 - 15:30
Effective and efficient gaussian processes
Wednesday 07 Aug 2019
Time: 10:00 - 18:00
Open source challenges in the enterprise
Thursday 04 Jul 2019
Time: 17:30 - 19:00
Distributed ledgers for engineered systems: hype or hope?
Wednesday 27 Sep 2017
Time: 18:37
Engineering Applications of Blockchain Technologies
Tuesday 31 Jan 2017
Time: 09:30 - 19:00
Masterclass: High-performance and Cluster Computing with Microsoft Azure
Monday 23 Jan 2017
Time: 17:37
Publications and software
Towards Comprehensive Dependability-Driven Resource Use and Message Log-Analysis for HPC Systems Diagnosis
E. Chuah, A. Jhumka, S. Alt, D.B-Thomert, J.C. Browne, M. Parashar, "Towards Comprehensive Dependability-Driven Resource Use and Message Log-Analysis for HPC Systems Diagnosis", Journal of Parallel and Distributed Computing (JPDC), Elsevier, 2019.
Using Message Logs and Resource Use Data for Cluster Failure Diagnosis
Failure diagnosis for large compute clusters using only message logs is known to be...
E. Chuah, A. Jhumka, J. C. Browne, N. Gurumdimma, S. Narasimhamurthy and B. Barth, "Using Message Logs and Resource Use Data for Cluster Failure Diagnosis," 2016 IEEE 23rd International Conference on High Performance Computing (HiPC), Hyderabad, 2016, pp. 232-241. doi: 10.1109/HiPC.2016.035
Sparse communication for distributed gradient descent
We make distributed stochastic gradient descent faster by exchanging sparse updates instead of dense...
Aji, A & Heafield, K 2017, Sparse Communication for Distributed Gradient Descent. in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017). Association for Computational Linguistics (ACL), pp. 440-445, EMNLP 2017: Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 7-11 September.
Consensus in the Age of Blockchains
The blockchain initially gained traction in 2008 as the technology underlying Bitcoin [105], but...
Bano, S., Sonnino, A., Al-Bassam, M., Azouvi, S., McCorry, P., Meiklejohn, S., & Danezis, G. (2017). SoK : Consensus in the Age of Blockchains.
