Statistical Science


Knowledge Transfer

Knowledge Transfer in Statistical Science

The research mission of the Department is to identify, develop and apply the sound statistical foundations essential for advances in the physical, social, environmental and medical sciences. The majority of the Department's research is motivated by applications, and much of it is done in collaboration with scientists from disciplines outside Statistical Science, some of them academics, some of them from industry, commerce or government.

Current and recent examples include:

  • ELDEV addition: As part of the ELDEV project (“Financial Engineering Analysis of Electricity Spot and Derivatives Markets”), Afzal Siddiqui has the opportunity to collaborate closely with two Norwegian power companies in order to determine not only how financial methods can be used to improve business practices, but also what problems are being encountered by industry. By organising two workshops annually in Trondheim, Norway, we communicate our findings to other academic researchers and power industry representatives. In fact, the first conference, the ELDEV Winter Conference, was held on 11 – 12 February 2009 at the Norwegian University of Science and Technology and attended by several European researchers and Norwegian power company analysts. The second one was held on 4 - 5 March 2010. Hence, continuation of such activity will provide direct, tangible benefits for the power sector in adopting state-of-the-art decision support tools.
  • Several Statistical Science staff work in close collaboration with environmental scientists to disseminate knowledge between disciplines and to the wider community outside academia. The department is closely involved with the UCL Environment Institute and the Institute for Risk and Disaster Reduction . Examples of knowledge transfer activities in the area of environmental science include joint supervision of PhD students in the UCL Geography Department (current topics include investigation of the impacts of climate change upon drought in southern Africa, and a study of groundwater arsenic mobilisation in Bangladesh); co-organisation of a multidisciplinary research programme on climate prediction at the Newton Institute for Mathematical Sciences in Cambridge (Dr Richard Chandler); a NERC-sponsored project in collaboration with several other universities, the British Geological Survey, water companies and international partners, examining interrelationships between climate change and the water cycle (Dr Richard Chandler); contributions to a series of NERC-funded training workshops for environmental scientists (Dr Richard Chandler).
  • Professor Tom Fearn has worked over a long period of time on the application of multivariate statistics to calibration problems in near infrared spectroscopy, with an emphasis on applications in food and agriculture. This has involved many international collaborations with spectroscopists and food scientists, currently with researchers in the Department of Animal Production in the University of Córdoba, Spain. His efforts to communicate good statistical practice include a regular Chemometric Space column in NIR News, and training courses at many international conferences.
  • The research of Professor Valerie Isham and Dr Richard Chandler into the spatial and temporal modelling of UK rainfall has been funded over a number of years by DEFRA. Dr Chandler's GLIMCLIM software is currently being developed by a UK engineering company for routine use in engineering design. His other knowledge transfer activities include an introduction to the science of floods and droughts, given as part of the Royal Statistical Society's contribution to National Science Week in 2006; contributions to a series of NERC-funded training workshops Statistics for Environmental Evaluation; and the delivery of a short course Advanced Analytical Methods for Climate Research at the Institute of Atmospheric Physics in Beijing in 2002.
  • Jointly with Brandon Whitcher (CIC, GlaxoSmithKline) UCL pursues an EPSRC funded project on Modelling Complex-Valued Diffusion Tensor Imaging Data and Efficient Methods for Inference. Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) is an in vivo medical imaging technique, based on Magnetic Resonance Imaging (MRI) technology, that captures the diffusion of water molecules in tissue. The impediment of this diffusion process by nerves enables the characterisation of white matter structure and the measurement of quantitative descriptions of white matter integrity. DW-MRI has identified white matter alterations for a large number of conditions including Alzheimer's disease, Parkinson's disease, schizophrenia, neurological complications of HIV infection, autism, multiple sclerosis etc. The potential of DW-MRI to generate imaging biomarkers for disease progression opens the door to applications in the pharmaceutical industry for drug discovery and development. DW-MRI stands as one of the most important new technologies that will help us to improve our understanding of the complex structure of the brain. The project entails the development of statistical signal processing methods to improve local analysis of DW-MRI data.
  • The MSc Statistics trains statisticians for jobs in industry, commerce and government. Some part-time students on this course already have such jobs, and there are many recent examples of summer projects with external collaborators.