Research Profile

Our Research Groups

Research in the Department of Mathematical Sciences is organised into a number of groups interacting with each other and other departments. A brief description of the groups and their research interests is given below. Each group maintains its own web pages, and much more detailed information about research, postgraduate studies and seminars can be found there.

Applied & Computational Mathematics

Mathematical and Theoretical Particle Physics

Pure Mathematics

Probability and Statistics

The group has a wide range of interests in the mathematical analysis of partial differential equations and in magnetohydrodynamics.

Our research activities fall into the broad categories of quantum field theory, string theory and gravity, cosmology and solitons in field theory. The group's interests are complementary to those of particle physicists belonging to the Institute for Particle Physics Phenomenology, and together we form the Centre for Particle Theory.

The areas of research of the pure mathematics group include global analysis, arithmetic, differential and hyperbolic geometry, number theory, representation theory, topology and interactions of these areas with dynamics, physics, engineering and computer science (robotics).

The interests of the group cover a wide range of topics associated with probability and statistics. In particular, topics studied include Bayes linear methods, applied Statistics, analysis of designed experiments, probability, percolation and geometric probability and quasi-stationarity.

Excellent Research Impact

Dr Peter Craig, Professor Michael Goldstein and Professor David Wooff are winners of the Durham University’s Award for Excellence in Research Impact in 2014. Professor David Wooff leads a team of PhD statisticians in developing the “forecaster’s predictive analytics engine”, which shows retailers where and how to spend online advertising budgets to deliver the greatest profit.

Recent Publications

McRedmond, James & Xu, Chang (2017). On the expected diameter of planar Brownian motion. Statistics & Probability Letters 130: 1-4.
Patelli,E., Feng,G., Coolen, F.P.A. & Coolen-Maturi,T. (2017). Simulation Methods for System Reliability Using the Survival Signature. Reliability Engineering & System Safety 167: 327-337.
Yin, Y.-C., Coolen, F.P.A. & Coolen-Maturi, T. (2017). An imprecise statistical method for accelerated life testing using the power-Weibull model. Reliability Engineering and System Safety 167: 158-167.
Hoyle, D.M. & Fielding, S.M. (2017). Necking after extensional filament stretching of complex fluids and soft solids. Journal of Non-Newtonian Fluid Mechanics 247: 132-145.
Aslett, L. J. M., Nagapetyan, T. & Vollmer, S. J. (2017). Multilevel Monte Carlo for Reliability Theory. Reliability Engineering & System Safety 165: 188-196.