Summaries of interested data science users

A number of data science stakeholders have expressed an interest in New Approaches to Data Science call. Details are given below. It is not a requirement of the call to involve these organisations, but they are happy to be contacted.

Food Standards Agency

Please see the Information Day report. Anyone interested in working with the FSA please contact Sian Thomas at informationmanagement@foodstandards.gsi.gov.uk

Tesco

Please see the Information Day report. Anyone interested in working with Tesco please contact Jeremy Bradley at Jeremy.Bradley@uk.tesco.com

EDF Energy

EDF Energy R&D UK Centre is interested in developing new research collaborations through student internships, PhD or EngD funding, and access to its datasets. It could offer the opportunity to apply or test research results on EDF Energy use cases.

Please contact Thibaut Possompes at thibaut.possompes@edfenergy.com for further information.

Dyson

Dyson is currently fact finding, looking at what it can do with existing techniques and what areas require new techniques. Some areas of interest include robotics and human behaviour understanding.

Anyone interested in exploring ideas with Dyson please contact Kay Yeong at kay.yeong@dyson.com

Intel

Data science is already playing a crucial role in the 21st century. With the right combination of people and technology, big data has the potential to solve big problems in public health, medicine, science, agriculture and engineering. Intel would welcome the opportunity to explore and partner with interested parties for this EPSRC call.

Anyone interested in working with Intel please contact Robert Maskell at robert.maskell@intel.com

Microsoft Research

Microsoft Research is keen to engage with applicants on a case by case basis to offer access to dedicated cloud computing resources through its Azure for Research programme. Microsoft Azure supports Jupyter notebooks, big data analytics, machine learning, and open-source applications and frameworks (such as Linux, Python, Java and Docker). It enables you to run large memory virtual machines, take advantage of InfiniBand-enabled high performance computing, GPUs, IoT, and big data platforms, such as Spark and Hadoop, easily and without queuing behind other users. It is ideal for sharing your work, and making research reproducible.  Microsoft is working with the Alan Turing Institute on using Microsoft Azure for its data science research, and is keen to work with applicants to this call to maximise joint opportunities for collaboration.

Contact Dr Kenji Takeda to discuss this support for your project: Kenji.Takeda@microsoft.com

Network Rail

We are the owner and operator of most of the rail infrastructure in Great Britain (England, Scotland and Wales) including 32,000 km of railway tracks, signals, overhead wires, tunnels, 32,000 bridges, level crossings and most stations, but not the passenger or commercial freight rolling stock. Furthermore, we own 2,500 railway stations, and manage 18 of the biggest and busiest of them.  We are generating increasing amounts of data from all of the assets across our network.  As an example of the uses for data science we are increasingly moving to a system of risk-based maintenance that allows us to monitor our assets, judge how well they are performing and then decide when to carry out maintenance work or renewals based on real-time, accurate data and judgements about criticality.  We strive for a technology enabled future.

Anyone interested in working with Network Rail please contact Mark Gaddes: 07710 958 081  / Mark.Gaddes@networkrail.co.uk

AWE

There are several grand challenges in data science driven by national security requirements. In simple terms, the growth in data science is a result of the increasing need to gain new insight from the vast quantities of information gathered, whether in the last 10 years of the information age or through decades of scientific endeavour and discovery. In terms of providing, maintaining and ensuring the safety and performance of the UK’s nuclear deterrent, there are particular applications in the study of probabilistic numerics, data visualisation and manipulation, and the tools for understanding large datasets generated through supercomputing and using simulation techniques. With current compute performance measured in Petaflops (millions of billions of operations per second) and the expectation of Exaflops early next decade, further exploration within scientific data has become the grand challenge for significant data reduction and analysis! AWE scientists and engineers are seeking to address some of these current and future challenges through expertise, innovation and collaboration with UK academia and industry, and specifically with developers of new data analysis software tools and new mathematical and scientific methods.

The AWE contact details for any follow-up are:

  • Professor Andrew Randewich
    AWE chief scientist
    Direct: 0118 98 55018
    AWE, Aldermaston, Reading, RG7 4PR
  • Dr Norman Godfrey
    Deputy chief scientist
    Direct: 0118 98 24368
    AWE, Aldermaston, Reading, RG7 4PR