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Biography
Dr Chibuzor Christopher Nnanatu (a.k.a. Chris) is a Senior Research Fellow in Spatial Statistical Population Modelling (SSPM) within the WorldPop research group, School of Geography and Environmental Science, University of Southampton, UK.
Following a successful completion of his PhD statistics studies at Lancaster University in 2018, Chris has acquired a diverse teaching and research experience. Before joining WorldPop in January 2022, Chris worked as a Senior Statistician with the Centre for Environment, Fisheries and Aquaculture Science (CEFAS), UK, from 2019 to 2022 - where he led, co-led and contributed in the initiation and delivery of a number of key fisheries/ecological research/policy projects.
In 2020, Chris was appointed an Associate Lecturer in Statistics with the department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle, UK - where he had previously worked as a Research Fellow in Biostatistics 2018 to 2019 on a UKaid funded project entitled "Evidence to end Female Genital Mutilation/Cutting (FGM/C)".
Additionally, Chris holds MSc in statistics (2011) and BSc in statistics (2006; second-class, upper division) from Nnamdi Azikiwe University, Awka, Nigeria where he worked as a Lecturer in Statistics. During his PhD statistics studies at Lancaster University, Chris successfully attended all the key intensive short courses in statistics powered by the Academy for PhD Training in Statistics (APTS) across four top UK universities including the University of Cambridge, University of Warwick, University of Southampton, and University of Glasgow.
He was a Graduate Teaching Assistant (GTA) in the department of Mathematics and Statistics, Lancaster University, UK, from 2014 to 2018 where he also worked as a Research Assistant in Statistics between 2015 and 2017.
Chris is presently the Spatial Statistical Population Modelling (SSPM) Team Lead, within the WorldPop, University of Southampton. His research interests are on Bayesian statistical measurement error models for population data; Novel statistical techniques for multiple-source data integration; Geospatial modelling and mapping techniques for child and maternal health issues; infectious disease models; computational statistics and machine learning. He is actively looking for PhD Students.