The Statistics group carries out methodological research in contemporary issues in computational and mathematical statistics, as well as investigating applications of statistics to the natural, biological and social sciences, including engineering, medical imaging, materials science, ecology, and testing theory.
Dr Eugénie 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
interested in the science of data, Data Science—data and modelling are the foundation of modern science. His research activities lie on the intersection of Data Science and subject matter sciences, including biodiversity, ecology, biology, public health, social sciences and engineering.
Dr Diwei Zhou
is interested in developing statistical methodology for large or highly-structured data analysis with applications to medical image analysis, computer science and engineering. She has methodological interests in Bayesian Statistics, Computational Analysis, Shape Analysis and Medical Statistics.
- Dr. Safa Elsheikh (supervised by Diwei Zhou, funded by HEIF): Development of Methods and Tools for Diffusion Tensor Image Analysis for Muscle Studies.
- Stefan Calvert (supervised by Diwei Zhou and Steven Kenny from Materials): Data Analytics and statistical modelling for engineering data.
- Steff Farley (supervised by Eugenie Hunsicker): Image Metrics for Statistical Data Integration.
- Fiona Houlgreave (supervised by Hideyasu Shimadzu and John Ward from Maths): integrating statistical and ecological frameworks to quantify biodiversity related challenges.
- Kerry Rosenthal (supervised by Eugenie Hunsicker and colleagues from SSEHS) : Statistical Methods for Metabomics with Gas-Phase Mass Spectrometry.
- Lei Ye (supervised by Diwei Zhou, Eugenie Hunsicker and Baihua Li from Computer Science): statistical data mining given brain and musculoskeletal images.