David A Wooff: Research Interests


My research interests arise (1) out of a long collaboration with Michael Goldstein and others on developing Bayes linear methods, (2) out of collaborations into interesting topics with colleagues in other University of Durham science departments, (3) as consequences of the consultancies I carry out on industrial and commercial projects. More specifically, my current research activities are in the following areas.

Current research students.

  • Mohammadamin Jamal-Zadeh, PhD. Amin will be working on Bayesian approaches to large-scale data sets.
  • Faiza Ali, PhD. Faiza is working on a Bayes linear approach to comparisons for imcomplete pairs.
  • Grace Stirling, M.Sc. Grace is working on the statistical planning of resource for urgent care such as GP out of hours call-centres. This is part of a Knowledge Transfer Partnership with Northern Doctors Urgent Care Group.

Former research students.

  • J. Argyle, PhD. Statistical analysis of child growth data.
  • L. Dow, MSc. Managing the uncertainties in commodity trading: a Bayesian software implementation.
  • J.A. Cumming, PhD. Clinical decision support.
  • Anesee Ibrahim, PhD. Evaluating training effectiveness in the Malaysian public service.

The general development and application of Bayes linear methodology.

For more details see the Bayes linear methods home page. Michael Goldstein and I have written a book, Bayes linear statistics, for Wiley, which appeared in April 2007.  

The application of modern statistical techniques to explore the environmental impact of pesticides.

This research is a collaboration between statisticians and an environmental chemist, and largely concerns whether it is possible to use the chemical properties of pesticides to predict a pesticide's potential to pollute the environment. This is a very rich area for future research as there are major shortcomings with current practice, and considerable though addressable difficulties with data sources and statistical modelling of the underlying relationships.

Prediction and diagnostics for child growth.

This concerns a study carried out by Jenny Argyle for her PhD, supervised by Allan Seheult and myself. The basic question of interest is whether it is possible to detect at an early stage a failure to thrive in infants by examining the individual child's growth curve and relating the curve to national standards and the child's growth history. The main results are a prediction method for child growth, and the finding that the correlation structure between successive measurements is Markov.

The statistics of mental health and learning disability.

This research, which arose initially from internal consultancy, is collaborative with John Carpenter and Justine Schneider of the Centre for Applied Social Studies, University of Durham, and others. It concerns several explorations and assessments of data relating to potential policy decisions affecting the care of people with mental health or learning disability problems. Many papers presenting the results of the research have been published or are in submission.

One current project concerns the use of Bayesian belief networks to model relationships for social policy and to support decision making. This began as methodology used to model variables affecting good practice in the support of people with some form of disability, for example learning disability or mental health problems. In particular, the aim was to place such people in worthwhile and satisfying employment. This research was funded by the Department of Health and lead to methodology which could be implemented in the form of a computer tool. This tool is now being developed to commercial prototype, part funded by the charity SCOPE and part-funded by the University of Durham Partnership Venture Fund. The project is called Supported Employment Assessment & Audit Tool.

Applying Bayes linear methodology to software testing.

This work concerns a major research study in collaboration with British Telecommunications Plc and statisticians and computer scientists at the University of Durham. The basic aim of the work is to arrive at an improved statistical methodology for treating software testing, exploiting the expertise of BT staff, in order to arrive at even more reliable software, more quickly and more cheaply than current methods allow. I was co-investigator in the EPSRC-funded project High reliability software testing for complex software using Bayesian graphical modelling and program comprehension which arose from the ongoing industrial collaboration.

A prototype tool has been constructed to implement the research. Funding for this has been provided jointly by British Telecommunications Plc and Codeworks Ltd, http://www.codeworks.net/.


Diagnostic and graphical modelling for sub-arachnoid haemorrhage.

This work concerns a study in which statisticians (myself, Frank Coolen, Malcolm Farrow) are working with experienced physicians in Accident and Emergency departments in the main North Eastern hospitals to improve our understanding of sub-arachnoid haemorrhage (SAH). This is a rare but often fatal disease involving the bursting of blood vessels at the periphery of the brain. It is hypothesized that ``warning leaks'' (minor haemorrhages) can precede major (usually fatal) haemorrhages. Properly diagnosed warning leaks lead to treatment which can prevent a further major haemorrhage. However, there is a suspicion that such warning leaks, for which the major symptom is severe headache, are often misdiagnosed for a variety of reasons. The work is thus aimed at exploring whether it is possible to identify particular combinations of patient signs and symptoms that indicate SAH with high probability. The approach we take is to exploit the Bayesian graphical model structures established by David Speigelhalter, Stefan Lauritzen, and others.

Bayes linear strategies for commodity trading.

This is work aimed at developing methods to support the decision making of commodity traders, in particular by understanding better the uncertainties involved. This work has been funded by the EPSRC as a Knowledge Transfer Partnership (number 38) with Energy Scitech Ltd..

Clinical decision support.

This  relates to PhD work carried out by Jonathan Cumming., as an industrial CASE studentship, funded by EPSRC and sponsored by the Smith Institute. The collaborating company was Etech Ltd. The background is that physicians and surgeons collect vast amounts of data on patients, often taking the same sets of measurements before and after (sometimes repeatedly) an intervention - for example, an operation. The question is whether anything useful can be gleaned from the data. In particular, do the measurements indicate that the operation was effective; do the measurements imply that one operation type was more effective than an alternative, and so forth. The kind of support offered is two-fold. First, better ways of examining large amounts of data in parallel, using graphics. Secondly, making sense of the data by structuring it appropriately and then modelling and exploiting relationships between the variables concerned.



Department Address:

D A Wooff
Dept. of Mathematical Sciences
Science Laboratories
Stockton Road


(0191 or +44 191) 334 3121


(0191 or +44 191) 334 3051