Statistics, inverse problems, uncertainty quantification and data science

Our researchers combine statistical and mathematical models, data science and computational algorithms to solve real-world problems.

To tackle the complex challenges of modern society, and due to increases in available computing power and data, traditional disciplines in statistics and applied mathematics are merging into exciting new inter-disciplinary fields.

Building on a strong tradition of statistics and numerical analysis in Manchester, our researchers work in a range of inter-connected areas, combining statistics, mathematical models, data science and computational algorithms to solve problems arising in engineering, life sciences and public health.

From methodological research to data analysis and development of software, we have expertise in the following core areas:

Areas of expertise

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  Statistics

  Statistics is concerned with the analysis and interpretation of data, the design of experiments and decision-making under uncertainty.

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  Inverse problems

  In inverse problems researchers look inside solid objects or deduce complex models from data using mathematics.

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  Uncertainty quantification

  Uncertainty quantification is a modern inter-disciplinary science that cuts across traditional research groups and combines statistics, numerical analysis and computational applied mathematics. 

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Our staff, students and postgraduate researchers have access to a fantastic range of facilities across the University.