Mathematical Sciences

Research

Statistics

The Statistics group is involved in methodological research in contemporary issues in mathematical and computational statistics, as well as in making diverse applications to the natural, biological and social sciences, including engineering, medical imaging, astrophysics, materials science, ecology, testing theory, etc.

Academic staff within this group are:

  • Dr Dalia Chakrabarty is interested in Bayesian hypothesis testing, graphical models and Bayesian learning given different data situations, particularly, tensor-shaped data and missing training data.
  • Dr Eugenie Hunsicker is interested in geometric and topological approaches to inference for high dimensional data and image data, as in data from the physical sciences and engineering. She is particularly interested in relationships amongst geometric statistics, topological data analysis and spatial statistics.
  • Dr Hideyasu Shimadzu's general research interest is in the science of data, Data Science. Data and modelling—these have been the foundation of the modern sciences. His research activities lie at the intersection of statistics and subject matter sciences, with a particular focus on environmental/ecological sciences. His research concerns how statistical considerations contribute to advancing our knowledge and understanding of phenomena of interest.
  • Dr Diwei Zhou works in the area of Statistical Medical Imaging with applications to brain and musculoskeletal studies and she has methodological interests in Bayesian Statistics, Computational Statistics, Shape Analysis and Medical Statistics.
  • Dr Emily Petherick at the School of Sport, Exercise & Health Sciences, and Prof. Baibing Li at the School of Business & Economics, are also associates of the Statistics group.

PhD students in the group include:

  • Cedric Spire: Bayesian learning in the absence of training data.
  • Richard Farley:
  • Kangrui Wang; high-dimensional Bayesian supervised learning using tensor-variate Gaussian Processes & graphical models.
  • Ben Cooper (co-supervised by Swidbert Ott—Animal Behaviour, University of Leicester): phase separation of locust species.
  • Lei Ye: statistical data mining given brain and musculoskeletal images.
  • Safa Elsheikh: computational statistics with human brain diffusion tensor images.
  • Jiajia Yan: statistical estimation given diffusion MRI data.
  • Fiona Houlgreave (co-supervised by John Ward—Maths): integrating statistical and ecological frameworks to quantify biodiversity related challenges.