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Prof Camila Caiado

Profile: Durham University Profile (External Link)

Email: c.c.d.s.caiado [at]

I’m a Bayesian statistician. I was born and raised in Brasilia (Distrito Federal, Brazil). I obtained a BSc in Statistics from the University of Brasilia in 2006 and started looking for postgraduate opportunities. I then moved to Durham (September 2007) to start my PhD in Statistics in the Maths and Earth Sciences Departments.

I joined the Leverhulme funded Tipping Points project here in Durham in 2011 as a postdoctoral research associate. It was a great opportunity to learn about other disciplines and meet many interesting people in and outside Durham.I became a lecturer in the Stats group in the Maths department in April 2015; I have since been promoted to Professor. I am currently the Director for Interdisciplinary Postgraduate Taught Programmes for the Faculty of Science and also the Director for the Masters of Data Science (MDS).

My main research interests are in Bayesian approaches to modelling and uncertainty quantification. I am mostly interested in the development and implementation of models and the design of emulators (statistical representations) for large complex systems such as health, climate, and population dynamics.

My current research is focused on multi-model uncertainty looking at frameworks for assimilating multiple models and experts’ beliefs, the aim of these frameworks is to unify multiple uncertainty specifications and provide an accessible decision support mechanism. This approach is essential when studying systems such as health where fast and reliable tools are necessary to aid decision making or such as climate where different modeling approaches are used by experts in different areas to inform policy makers.

My current collaborations involve the development of Bayesian methods and their application to a number of areas including health, engineering, societal dynamics, climate, seismology, and banking. Most of these partnerships are generating substantial outputs with current and eminent impact in the local industry and society.