I joined the Statistics and Probability group of the School of Mathematical Sciences at the University of Nottingham in September 2006 as a Research Fellow. I am currently an Associate Professor in Statistics.
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I have been invited to speak at the SPHINx conference which will be held at the Conservatoire national des arts et métiers (Cnam) in Paris (France) from June 24 to June 25, 2019. The conference aims at providing an overview on recent advances and challenges in the mathematical modelling of pathogen spread in healthcare settings.
I presented our more recent work Bayesian Non-Parametric Inference for stochastic epidemic models for heteregenously mixing population.
My research is concerned with the development of novel statistical methodology for Bayesian inference and model selection for high-dimensional complex data with a particular focus on designing stochastic epidemic models and fitting them to infectious disease outbreak data.
My resume can be found here .
Together with Phil O'Neill I have been an instructor for the module entitled "MCMC II for Infectious Diseases" at the Summer Institute in Statistics and Modeling in Infectious Diseases since 2010. This year our module will be delivered between Monday 22nd of July and Wednesday 24th of July.
This academic year I am teaching (the first of part of) Statistical Models and Methods (G12SMM/MATH2011) module in the Autumn, and the Data Analysis and Modelling (G14DAM/MATH4067) modules throught the year. Students who are currently taking either of these module will find all the relevant information on the modules' moodle pages .
G12SMM/MATH2011: Statistical Models and Methods: The first part of this module provides an introduction to statistical concepts and methods. A wide range of statistical models will be introduced to provide an appreciation of the scope of the subject and to demonstrate the central role of parametric statistical models. The key concepts of inference including estimation and hypothesis testing will be described. Special emphasis will be placed on maximum likelihood estimation and likelihood ratio tests. While numerical examples will be used to motivate and illustrate, the content will emphasise the mathematical basis of statistics. Topics include maximum likelihood estimation, confidence intervals, likelihood ratio tests, categorical data analysis and non-parametric procedures.
G14DAM/MATH4067 Data Analysis and Modelling: This module involves the application of probability and statistics to a variety of practical, open-ended problems, typical of those that statisticians encounter in industry and commerce. Specific projects are tackled through workshops and student-led group activities. The real-life nature of the problems requires students to develop skills in model development and refinement, report writing and teamwork. Students will have an opportunity to apply a variety of statistical methods and knowledge learned in previous modules taken at level 1 and level 2.
In previous academic years I have taught several modules, the details of which can be found on my Resume.