Current collaborative research activities
EPCC undertakes a wide range of collaborative research projects with both academia and industry.
We are the coordinating partner of several major European Commission Framework 7 and Horizon 2020 projects such as Fortissimo and a partner in many more. Further information on our research collaborations can be found in our Research section.
Industry vendor collaborations
Our collaborations with blue chip technology vendors place us at the leading edge of HPC, big data analytics and artificial intelligence. Current partners include:
Cray. Through the Exascale Technology Centre, we work with Cray to explore new ideas and new technologies to meet the challenge of delivering an Exaflop within the next decade.
Intel. EPCC is an Intel Parallel Computing Centre (IPCC). This collaboration sees EPCC working to optimise a range of large-scale simulation codes for Intel Xeon and Xeon Phi processors.
SGI. We have been chosen to host a European centre for Silicon Graphics International (SGI). The SGI European Research Centre will be co-located within EPCC and will explore high performance computing issues related to healthcare, materials science, climate change and renewable resources, among others
NVIDIA. We have developed strong links with this global technology company, and EPCC is an NVIDIA Centre of Excellence utilising large-scale GPU infrastructure to further research in AI and machine learning.
Collaborative research projects
Recent and current project partners include:
The pay-TV subscription, billing, customer relationship management (CRM) specialist partnered with EPCC to drive the development of new AI-driven capabilities in its next-generation subscriber intelligence platform.
News story: Paywizard and EPCC announce partnership to drive new AI features
We worked with this privately-owned engineering company on the research and experimental development of neural networks and supporting frameworks for the identification of marine mammal species from video images provided in a restricted set of circumstances.
We provided software expertise to this technology start-up to integrate machine-learning and back-end functions into a robust multi-user interface which can cope with ultra-high volumes of queries and process large amounts of data quickly.
Rock Solid Images (RSI)
This project used machine learning to optimise the process of petrophysical interpretation.
Blog post: Machine learning for oil & gas exploration
EPCC provided software and data architecture expertise to this fast-growing fintech start-up to support its rapid growth plans as it scales its offering across the open banking network.
Roomerical offers wave-based room acoustic simulation in the cloud. We collaborated with its founders Dr Brian Hamilton and Prof Stefan Bilbao (University of Edinburgh's Acoustics and Audio Group) in the exploration of numerical simulation techniques for architectural acoustics, which led to the development of Roomerical's high-performance acoustic ray-tracing module, to be used in conjunction with their wave-based acoustic simulations.
Blog post: High-performance ray tracing for room acoustics
Exnics is a provider of flow surveillance systems to the Oil & Gas sector. EPCC will undertake a feasibility study into the development of an algorithm to classify and measure flow conditions inside a pipe by non-intrusively monitoring minute optical variances of monochromatic light observed at the pipe surface.
We worked with this Edinburgh-based Oil & Gas SME to determine the feasibility of predictive models for electrical submersible pump operations using new and novel machine learning techniques.
A consortium led by Rolls-Royce and EPCC was awarded an EPSRC Prosperity Partnership worth £14.7m to develop the next generation of engineering simulation and modelling techniques, with the aim of developing the world’s first high-fidelity simulation of a complete gas-turbine engine during operation.
Blog post: New Prosperity Partnership to develop world first in high-fidelity engineering simulations
Orbital Micro Systems (OMS)
Small satellite technology provider OMS will work with EPCC and the University's Schools of Geosciences and Informatics to design data and analytics technology for the sensors of a satellite-based system that will vastly improve global monitoring and forecasting of extreme weather and natural disasters.
News story: EPCC drives satellite development
Global Surface Intelligence (GSi)
GSi is a data services company that provides machine learning and predictive analytics based on large and complex data sets. Its services include analysing satellite information and remote-sensing Earth Observations. EPCC re-engineered GSi's software to enable it to run on our HPC systems, and the company now uses our HPC platform as a service to provide the basis of its customer-facing large-scale data management and analytics services.
GSi project overview
National Biodiversity Network's (NBN) Atlas of Living Scotland
EPCC and NBN (along with Scottish Natural Heritage and SEPA) have been collaborating to provide an Automated Data (Layer) Harvester for the NBN Scotland Atlas. The Atlas combines multiple sources of information about UK species and habitats, and the ability to interrogate, combine, and analyse these data – in a single location – has not been achieved before on this scale. Currently it must be be updated manually every time a provider of a spatial data layer makes changes to a layer. The Automated Data (Layer) Harvester checks for changes in a provider’s layer and automatically pulls the data layer into the Atlas and uploads it.
EPCC and Intel collaborated with retinal imaging company Optos to significantly enhance and optimise a software algorithm for use in a new medical product under development. The result is software which runs 15 times faster than the original software-hardware combination, while using an Intel chip that is 2.5 times cheaper. This combination allows the company to cut costs while enhancing the performance of its product.
Optos case study.
Alan Turing Institute-Scottish Enterprise Data Engineering Programme projects
EPCC worked with Melissa Terras (College of Arts, Humanities and Social Sciences (CAHSS), University of Edinburgh) and Raquel Alegre (Research IT Services, University College London (UCL)), to explore text analysis of humanities data. The goal of this collaboration is to run text analysis codes developed by UCL upon data used by CAHSS to exercise the data access, transfer and analysis services of the Turing's deployment of a Cray Urika-GX system.
Blog post: Analysing humanities data using Cray Urika-GX
EPCC, the Alan Turing Institute and a major airline are jointly investigating how airlines can increase revenue from passenger flights. EPCC staff will engineer the required data into suitable structures and formats for revenue analysis and modelling.
EPCC is collaborating with the Alan Turing Institute to optimise software for the analysis of complex data graphs on state-of-the art processors. Large-scale analysis of graphs is fundamental to a number of fields, from cyber security to biology.
Proof-driven querying (PDQ)
We are carrying out research into PDQ together with Alan Turing Institute staff from the University of Oxford. A target application for this research is making confidential data (eg from the NHS) accessible to data scientists while respecting constraints imposed by privacy, integrity and efficiency.
We are collaborating with the Alan Turing Institute to build, configure and operate a Data Safe Haven to support the secure analysis of confidential data.