Dr. David C. Hill

Trained originally as a mathematician, with a 1st Class Hons degree, David has around 30 years' experience in maths and stats modelling in a variety of fields, such as energy and the environment, but which also includes 9 years' experience in medical statistics.

Educated at the University of Exeter where he did his first degree in Mathematics, which included courses in statistics, he went on to do a MSc by Research (in Space Science) at the University of Oxford. Gaining experience in the early days of the wind energy industry, working on an aerodynamical study of wind turbine blades for the then Garrad Hassan and Partners, and the Rutherford Appleton Laboratory in Chilton, he returned to academia to complete a PhD in ocean science at the University of Wales, Bangor in 1999. The thesis involved the creation of an inverse method incorporating an optimisation procedure to estimate sediment parameters such as erosion rates and fluxes (e.g. into the North Sea through the Dover Straits). An iterative calibration procedure was applied to an Acoustic Doppler Current Profiler that measured sediment concentration at different heights above the sea bed. He also has tidal modelling experience.

After a short project in climate change detection and attribution, utilising linear methods called optimal fingerprinting to distinguish between anthropogenic and natural climate signals in vertical temperature data, he commenced his interest in medical applications employed by Cancer Research UK (CRUK), based at the University of Oxford, to work on health risks from residential radon gas. This work handled large datasets including the 7,148 cases and 14,208 controls, and was a pan-European project involving data and collaborators from 9 countries. He then became engaged in further work in time series analysis for prediction of wind speeds and in wind energy grid integration studies at the University of Strathclyde, Glasgow. This centred around the use of Vector Auto-Regressive (VAR) models for wind integration studies to calculate capacity factors, and for wind speed prediction. This project also involved the downloading and processing of data from the pan-European COSMO meteorological model, the British Atmospheric Data Centre's MIDAS meteorological data and the use of the MERRA reanalysis dataset so he has much experience of handling large meteorological datasets. He is now extending his knowledge to nonlinear time series analysis.

This preceded another medical stats role at Stirling and Glasgow Caledonian Universities involving applications employing a range of statistical techniques such as logistic regression in mixed effect models, network maps and forest plots in meta-analyses of trials. These were applied to studies in maternity care and female urinary incontinence. He also carried out research into nursing interventions using linked data from the Safe Haven at Dundee and involving a stepped wedge trial design. He continued in medical statistics at the University of Birmingham where he was responsible for all descriptive tables that fed into analysis for which the primary objective was to see whether a prognostic model based on perfusion computed tomography (PCT) is better than current practise.

David has a diverse background, not only in medical statistics but in environmental science including climate change with a PhD in ocean science and much experience in wind energy applications.

David is a skilled programmer, with expertise in the statistical packages Stata, 'R', and is proficient in MatLab. He enjoys working in a team or independently developing applied software. He hopes to draw from his diverse experience to creatively apply his skills in new fields of research for Opus Numerics, including in nonlinear time series analysis.