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My background in Physics (BSc), and Statistics (MSc) led to be to strive to develop novel statistical and machine learning methods to tackle real-world problems.

My PhD involved the development of a new Bayesian methodology to tackle learning in the absence of training data, with a direct application to astrophysical data.

I am currently on a Postdoctoral Fellowship (RAEng UK IC 2019), working on the development of anomaly detection and correction methods for diverse data types, on data provided by a public sector organisation. 


PhD in Mathematics - University of Loughborough

MSc in Statistics - University of Nottingham

BSc in Physics - University of Nice-Sopha-Antipolis (France).

Newest selected publications

Spire, C. and Chakrabarty, D. (2019) 'Learning in the absence of training data—A galactic application'.International Conference on Bayesian Statistics in Action. Warwick, United Kingdom. 22 - 3 July. Springer International Publishing. pp. 43 - 51. ISSN: 2194-1009 Open Access Link

Conference paper
More publications(1)