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Courses

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Research

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People

We are different and we are the same. #WeAreEssex

Something else

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School of Mathematics, Statistics and Actuarial Science

Our Research

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New knowledge, new solutions

We're an interdisciplinary school with impact on a diverse range of areas from social policy to insurance risks, and human-technology interaction to mathematical physics.

Research in our school is organised into data science, and mathematics. Both groups reflect the major fields of mathematical research undertaken by our staff and cover specific areas of research interest.

Staff in our research groups work closely both in the school and with academics across the wider university to develop strong interdisciplinary research.

Our work in Knowledge Transfer Partnerships gives us a strong commercial impact and ensures that businesses benefit from cutting-edge research.

Our academics also act as supervisors for postgraduate research degrees in their specialist areas, and co-supervise research students working on interdisciplinary projects with colleagues in other departments across the university. PhD students in the school can join one of our research groups during their time with us.

Our school holds weekly research seminars during term time. These seminars often involve guest speakers from other institutions and are open to students and academics at Essex.

Groups and areas

Data Science

Group: Data science Area: Actuarial Mathematics Area: Data Science and Statistical Learning Area: Operational Research Area: Statistical Methodology

Mathematics

Group: Mathematics Area: Algebra, Geometry, and Discrete Mathematics Area: Analysis and Mathematical Physics Area: Applied Mathematics Area: Mathematics Education

Highlights of our research

Our projects

Our research is interdisciplinary and has impact across areas as diverse as social care, mathematics education, and animal welfare, with examples including:

Catalyst Project - Improving community services for vulnerable people - A group of researchers from our school contributed to the "Identifying 'at risk' adults and children" initiative in this project.
Machine learning for recognising tangled 3D objects - Funded by the Leverhulme Trust, this project will use machine learning to look at how we can teach computers to understand the 3D world, without using deep neural networks.
Cow tracking - We're combining animal behaviour data, gathered using wireless tracking sensors, with mathematics, to help predict early signs of illness or other medical episodes in cows. By giving farmers an early indication of problems, we can improve cow health and welfare.
Algorithmic fair division in dynamic, socially constrained environments - In this project we are developing a computer algorithm that will ensure fair division of indivisable assets between parties that have a claim on the asset (such as a valuable painting in a divorce case).
Demand forecasting and dynamic pricing for e-grocery fulfilment - This Knowledge Transfer Partnership project, run with online delivery company Ocado, will improve delivery efficiency using Big Data, machine learning and forecasting.
The Calabi problem for smooth Fano threefolds - This project studied the existence of natural notions of metrics on most three-dimensional positively curved shapes. Known as Fano varieties, these shapes are one of three kinds of building blocks of all other shapes and have a very rich geometry.
Random-field effects in spin models: Supersymmetry, criticality, and universality - This project aims at clarifying the effects of random fields, one of the most common yet less understood types of disorder with many experimental analogues in physics, on a variety of spin models and unveiling their critical behaviour and universality principles.
Stability of Brunn-Minkowski inequalities and Minkowski type problems for nonlinear capacity - This project is devoted to studying the stability of Brunn-Minkowski inequality for nonlinear capacity associated to A-harmonic PDEs for convex compact sets, as well as the related Minkowski problem.

Knowledge Transfer Partnerships

Our work can have direct impact and help businesses thrive through Knowledge Transfer Partnerships. Through KTPs our researchers can evidence the value and impact of their work, while businesses benefit from expertise and cutting-edge research techniques.

Some of our KTPs have included:

Working with the Port of Felixstowe to improve efficiency of complex scheduling processes. We assisted in the development of optimisation algorithms using artificial intelligence, which helped with both scheduling staff resources and solving scheduling problems. This reduced waste and improved customer service.
Carrying out a data science KTP with knowledge resource company Mondaq that helped them make the most of large data sets. They were awarded Winner of Best KTP Partnership 2018.
Members of our data science group are currently working on a KTP project with online grocery company Ocado, that will utilise Big Data with machine learning and forecasting to improve delivery efficiency, which will reduce cost to the company and the carbon footprint of deliveries.