Research themes within the Mathematics and Statistics Research Group (MSRG)

We focus on the following interrelated research themes within MSRG:

Authentic statistics e-assessments

Represented by Dr Iain Weir, Principal Lecturer; Dr Rhys Gwynllyw, Associate Professor; and Dr Karen Henderson, Associate Professor.

We have developed a method to create authentic statistics e-assessments allowing students to work on bespoke data sets generated efficiently and accurately using standard R routines, but delivered and marked using the Dewis e-assessment system.

Automatic marking of computer code

Represented by Dr Rhys Gwynllyw, Associate Professor; and Professor Jim Smith, Professor in Interactive Artificial Intelligence.

We have used Dewis to automatically assess programming skills, in particular, in C and Python. Marking is instant and provides students with intelligent feedback on their code.

Braess’ Paradox in transport networks

Represented by Dr Vadim Zverovich, Associate Professor.

Our research provides an extension of previous studies on Braess’ Paradox by considering arbitrary volume-delay functions.

Data science for transport

Represented by Dr Fiona Crawford, Lecturer in Statistics and Data Analytics.

We utilise a range of methods from data science to gain new insights into travel behaviour and to evaluate the impact of transport interventions. Projects have included an examination of the impact of the COVID-19 pandemic on road travel in Bristol, and the application of Market Basket Analysis to identify parts of the road network which are used by the same people.

Decision-support system for emergency response

Represented by Dr Vadim Zverovich, Associate Professor.

We developed an algorithm for finding the optimal routes for search and rescue teams in a building.

Diagnostic learning resources

Represented by Dr Rhys Gwynllyw, Associate Professor; Dr Karen Henderson, Associate Professor; Dr Emily Walsh, Senior Lecturer; Mr Basil Norbury, Senior Lecturer; and Dr Kevin Golden, Deputy Head of Department.

We have created diagnostic learning resources for several different student cohorts that provide mathematics support. Dewis has been used as the primary diagnostic tool and this provides additional feedback support in the form of signposting to learning materials based on areas that require attention.

Improving feedback and reliability of mathematics e-assessments

Represented by Indunil Sikurajapathi, Doctoral Researcher and Graduate Tutor in Mathematics.

We are developing our in-house algorithmic e-assessment system, Dewis, to improve feedback and reliability of Mathematics e-assessments. In particular, the research is focused on developing a method to detect Common Students Errors (CSEs) in Engineering Mathematics and to provide tailored feedback in the Dewis e-assessment system. Find out more about our  CSE Project. This project is supervised by Dr Karen Henderson and Dr Rhys Gwynllyw.

Mathematical models for disaster relief and humanitarian logistics

Represented by Dr Alistair Clark.

This research develops a robust network flow model to help decide how to rapidly supply humanitarian aid to victims of a disaster within this context.

Mathematical models for production lot sizing and scheduling

Represented by Dr Alistair Clark.

This research develops stronger formulations, as well as to incorporate real-world requirements from different applications.

Mathematical models of nurse shift scheduling and rescheduling

Represented by Dr Alistair Clark.

This research develops models for nurse rostering and re-rostering that consider nurses' preferences.

Maximising security in defence sensor networks

Represented by Dr Vadim Zverovich, Associate Professor.

Decision analysis can help to maximise security in sensor networks by developing and implementing autonomy within existing threat response procedures.

Model reduction methods in systems biology

Represented by Dr Lloyd Bridge, Senior Lecturer in Mathematics.

Asymptotic analysis and hybrid asymptotic-numerical approaches help to reduce high-dimensional differential equation models of biological processes to provide new insights into the core mechanisms of interest.

Molecular Dynamics (MD) simulation

Represented by Dr Mario Orsi, Senior Lecturer.

MD is a powerful technique to study matter at the molecular scale. We are especially interested in biomolecular systems.

Moving mesh methods

Represented by Dr Emily Walsh, Senior Lecturer.

The solution is a high proportion of mesh points in the regions of large-solution variation and few points in the rest of the domain. This means the total number of mesh points required is much smaller.

Multiple domination and limited packings in graphs

Represented by Dr Vadim Zverovich, Associate Professor.

We developed an application of the probabilistic method to k-limited packings in general and to 2-packings in particular.

New statistical approaches

Represented by Dr Paul White, Associate Professor (Applied Statistics).

Stochastic simulation is an ideal tool for assessing the behaviour of existing and newly proposed statistical techniques and methods. Based on past successful PhD work, we will consider the development of new bespoke approaches in the development of new methodology.

Numerical weather prediction

Represented by Dr Emily Walsh, Senior Lecturer.

A moving mesh method has the potential to resolve small-scale weather phenomena accurately and efficiently.

Operations and maintenance in offshore wind farms

Represented by Dr Xiaodong Li, Lecturer.

The maintenance cost reduction is a key theme in order to make offshore wind power more competitive in the energy market.

Optimal transport distance as a similarity measure for images

Represented by Dr Jan Van Lent, Senior Lecturer.

In our research, we have presented a numerical solution method and considered the application of image comparison.

Receptor dynamics and mathematical pharmacology

Represented by Dr Lloyd Bridge, Senior Lecturer in Mathematics.

Mathematical models of cellular signalling dynamics are needed in order to understand and characterise ligand-receptor interactions using new experimental time-course data. Our models typically comprise nonlinear differential equations or agent-based models.

Robustness of statistical tests

Represented by Dr Ben Derrick, Lecturer.

This research explores and presents alternatives to statistical techniques to provide robust solutions.

Structured life course modelling approach

Represented by Dr Andrew Smith, Senior Lecturer in Statistics.

Identifying statistical models that represent the relationship between an exposure measured over an individual’s life course and a later health outcome. Interpreting such models requires model selection and post-selective inference.

Using e-assessment for online exams

Represented by Dr Rhys Gwynllyw, Associate Professor; Dr Karen Henderson, Associate Professor; and Dr Iain Weir, Principal Lecturer.

We have developed robust testing regimes for using e-assessment for online exams under controlled conditions.

Using technology enhanced learning to support the student experience

Represented by Dr Rhys Gwynllyw, Associate Professor; Dr Karen Henderson, Associate Professor; and Professor Catherine Hobbs, Associate Dean, Faculty of Environment and Technology, UWE Bristol.

We are interested in evaluating the effectiveness of using technology enhanced learning in the teaching of mathematics. This includes the use of e-assessment, technology-enhanced active learning spaces, audience-response systems.

Mathematics and Statistics Research Group (MSRG)

Delivering internationally excellent mathematics research, including real-world impact; develop research collaboration; apply its research to outstanding learning and generate income.

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