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 and in 2008 I was appointed as Lecturer.
I am currently a Professor of Statistics and Head of the Statistics and Probability Section.
Please click on the icons below to either email me, book an appointment, or view my profiles on Twitter, Github and LinkedIn.
Another paper out of Rowland's PhD thesis has just been published in the Proceedings of the National Academy of Sciences on Bayesian nonparametric inference for heterogeneously mixing infectious disease models. The paper is available through open-access from the PNAS' website and there is also code available to fit our models to data on this Github repository.
I first joined the RSS Statistical Computing Section in January 2014 and served as a committee member until 2018. Since 2018 I had the privellege to be the Chair of the Section which then became the Computational Statistics and Machine Learning Section. My term as a Chair finished in December 2020 and I continued to serve the Section as a committee member until the end of 2021. However, I am still involved with RSS by sitting on the Academic Advisory Group committee.
In January 2020 I took over as Head of the Statistics and Probability Research Group and I have recently been appointed Head of the Statistics and Probability Section.
I presented a short talk at the 2021 ISBA meeting which took place online and gave an overview of the work of my former PhD student, Dr Rowland Seymour , on Bayesian non-parametric Inference for infectious disease models.
I presented a talk at the onilne BISP 12 workshop about my work on Latent Branching Trees, a novel class of semi-parametric time series models. You can watch my talk here.
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 including a full list of publications and invited talks can be found/downloded 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 was delivered between Monday 22nd of July and Wednesday 24th of July.
This academic year I am teaching the Statistical Inference 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.
MATH3013: This module is concerned with the two main theories of statistical inference, namely classical (frequentist) inference and Bayesian inference. The classical inference component of the module builds on the ideas of mathematical statistics introduced in MATH2011. Topics such as sufficiency, completeness and best-unbiased estimators are explored in some detail. There is special emphasis on the exponential family of distributions, which includes many standard distributions such as the normal, Poisson, binomial and gamma. In Bayesian inference, there are three basic ingredients: a prior distribution, a likelihood and a posterior distribution, which are linked by Bayes theorem. Inference is based on the posterior distribution, and topics including conjugacy, vague prior knowledge, marginal and predictive inference, normal inverse gamma inference, and categorical data are pursued. Common concepts, such as likelihood and sufficiency, are used to link and contrast the two approaches to inference. Students will gain experience of the theory and concepts underlying much contemporary research in statistical inference and methodology. There will also be an introduction to sampling-based inference using Markov Chain Monte Carlo (MCMC) methods using statistical software.
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.