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Welcome to The Centre for Probability, Statistics and Data Science

The centre covers three broadly overlapping main areas of research: probability, statistics and data science. Probability theory is a core topic within mathematics and a foundational aspect in much of the work of the centre. A broad range of areas within probability and applied probability are investigated from stochastic processes, understanding the properties of random mathematical structures including many applications to areas such as statistical physics, finance, etc. The centre also has a strong group of statisticians developing both statistical theory, e.g. Bayesian inference, methodologies, e.g. modelling of spatio-temporal data, and applications, e.g. biostatistics. Finally, the centre has in expertise of other aspects of data science including the foundations of machine learning, solving inverse systems as related to data, topological techniques, and others. 


Recent Publications

  • Papafitsoros K (2024). WildlifeDatasets: An open-source toolkit for animal re-identification. 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024)
  • Iacopini M, Poon A, Rossini L and Zhu D (2023). Bayesian mixed-frequency quantile vector autoregression: Eliciting tail risks of monthly US GDP. Journal of Economic Dynamics and Control, Elsevier vol. 157 
  • Kofler A, Altekrüger F, Antarou Ba F, Kolbitsch C, Papoutsellis E, Schote D, Sirotenko C, Zimmermann FF and Papafitsoros K (2023). Learning Regularization Parameter-Maps for Variational Image Reconstruction Using Deep Neural Networks and Algorithm Unrolling. SIAM Journal on Imaging Sciences, Society for Industrial & Applied Mathematics (SIAM) vol. 16 (4), 2202-2246.  

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