Lancaster University - Equipment Account

Lead Research Organisation: Lancaster University
Department Name: Unlisted

Abstract

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Publications


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Bardwell Lawrence (2016) Most recent changepoint detection in Panel data in ArXiv e-prints
Edwards J (2016) ON THE IDENTIFICATION AND MITIGATION OF WEAKNESSES IN THE KNOWLEDGE GRADIENT POLICY FOR MULTI-ARMED BANDITS in Probability in the Engineering and Informational Sciences
Fairbrother Jamie (2015) Scenario generation for stochastic programs with tail risk measures in ArXiv e-prints
Frick K (2014) Multiscale change point inference in Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Haynes K (2017) Computationally Efficient Changepoint Detection for a Range of Penalties in Journal of Computational and Graphical Statistics
Hofmeyr D (2016) Clustering by Minimum Cut Hyperplanes. in IEEE transactions on pattern analysis and machine intelligence
 
Description This capital equipment award funded videoconferencing and video-enabled lecture theatre infrastructure and well as high performance research machines. This equipment helps facilitate the research development and training of the STOR-i CDT students. The key outcomes of the award are best measured through the publications from the students, their industrial engagement, and the quality of careers that they progress to. The industrial partners they have undertaken their PhD projects with are list below. Papers are listed separately.

To give an indication of STOR-i's track record for student destinations the following, employing organizations include: British Airways, Dunnhumby, Shell, EDF Energy, Exient (then to Sega), FeatureSpace, JBA, Lubrizol, Summit Media and DrMemory (a local start up company), Sporting Data (a start up company). Several have also proceeded to post-doctoral research positions, with two currently appointed to lectureships.
Exploitation Route We are delighted that our vision for STOR-i students to take up leadership roles in Statistics and OR is starting to be realised. The overwhelming majority of the above industry positions explicitly use specific research skills developed in the PhD, together with broader generic Statistics and OR and career skills developed throughout STOR-i training. We are also beginning to see the STOR-i Fellowship programme providing a bridge to academic career paths.

PhD Projects have been with Aimia, ATASS, BT, DSTL, EDF, Jansen, JBA, Met Office, NNL, Roche, Rolls Royce, Shell, Sparx, with about half of these companies providing multiple projects.
Sectors Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Energy,Manufacturing, including Industrial Biotechology
 
Description The key impacts of the award come through the CDT students research papers and impact on their projects with industry listed elsewhere. Given the nature of the equipment purchased its direct impact is best explained by showing how it influences the working practice of the CDT students. STOR-i students regularly use the high quality video-conferencing facilities to support their supervision as all students have either an international academic partner or an industrial partner providing co-supervision. Developing such virtual communication and team-working skills is invaluable for tomorrow's research leaders, as many can expect to have future careers working in 'virtual' teams. The audio-visual equipment substantially enhances our training programme. It enables both video-lectures for our international masterclasses and seminars and the capture of training sessions. The ability to transmit lectures reliably from international speakers enhances the opportunity for STOR-i students to learn from the world's leading researchers who may not be able to visit the Centre. The vast majority of the STOR-i students are using our advanced high performance research machines to undertake large-scale optimisation, simulation and computationally intensive data analysis. This facility also addresses the data confidentiality issues that arise from our industry engagement .