About

I am a Statistician and Data Scientist at the University of Derby with expertise in Bayesian methods, computational statistics, complex systems, and spatial-temporal modelling. My research spans a range of applications including scaling problems, environment, health, crime, population, finance and disease.

I work on nationally funded projects, including collaborations with the NHS, and maintain an active international research profile with partners in Brazil and Australia.

I am a Fellow of the Higher Education Academy (FHEA) and hold Qualified Teacher Status (QTS).

Teaching responsibilities

I'm the module leader for all the statistics modules in our undergraduate Artificial Intelligence and Data Science portfolio, including Introduction to Probability and Statistics (first year), Statistical Methods for Data Analysis (second year), and Stochastic Processes (third year).

Beyond my teaching, I contribute to curriculum development, having led the creation of new statistics modules to support the growth of our Data Science and AI programmes. I also supervise student projects (BSc, MSc and PhD) and provide academic guidance as a personal tutor.

Research interests

I am broadly interested in Statistics and Data Science, with a focus on Bayesian inference, linear and generalized linear modelling, computational statistics, spatial and spatio-temporal methods, and time series analysis. My work applies these tools to diverse domains, uncovering hidden patterns, scaling behaviours, and dynamic processes across health, crime, environment, and society.

Current Research Highlights

• Health and Transparency: NHS expenditure data is used as a case study to reveal hidden patterns in government spending and the scaling behaviours of organisations and contractors.
• Crime Dynamics: UK crime outcome data is modelled using exponential decay processes to identify how investigations progress differently across police forces and crime types.
• Social and Economic Transitions: Population density is examined as a driver of social, health, and economic outcomes across England and Wales, highlighting rural–urban transitions and their underlying factors.
• Infectious Disease Modelling: Research investigates how city size and commuter networks shape disease spread in Brazil, and how COVID-19 propagated across England and Wales. A Bayesian generalised logistic regression framework was developed to capture skewness and heteroscedasticity, offering deeper insights than standard methods and a general template for studying complex dynamics.
• Environmental Systems: Wind speed and river depth data are analysed to distinguish short-term weather-driven behaviour from long-term climate-driven hydrological trends, supporting both short-term forecasting and long-term environmental understanding.

Membership of professional bodies

• Member of the Institute of Mathematical Statistics
• Member of the Royal Statistical Society

Qualifications

• PhD in Statistics – “Allometric and Fluctuation Scaling in Health, Well-Being and Mortality” – Nottingham Trent University
• MRes Mathematical Sciences – “Understanding the 3D Architecture of the Drosophila Genome” - Nottingham Trent University
• BSc (Hons) Mathematics and Secondary Education – Nottingham Trent University

Recent conferences

• NetSci2025 (Netherlands, Maastricht). Jack Sutton, Quentin Hanley, Gerri Mortimore, Ovidiu Bagdasar, Haroldo Ribeiro, Thomas Peron, Golnaz Shahtahmassebi and Peter Scriven. Evolution of Rural-Urban Scaling and the Interconnections between, mortality, crime, property transactions, road accidents and demographics.
• International Conference on Software and Information Engineering (Derby, December 2024). Hong Qing Yu, Jack Sutton, Sam O’Neill and Stephan Reiff-Marganiec. Case Studies on Large Language Models: Centric and Services Oriented Data Analytics Agent Development
• Royal Statistical Society (Aberdeen, September 2022). Jack Sutton, Golnaz Shahtahmassebi, Quentin Hanley and Haroldo V. Ribeiro. A heteroscedastic generalised logistic power law model with application to the COVID-19 pandemic scaling
• UN75+1 (Online, November 2021). Jack Sutton, Quentin Hanley, Golnaz Shahtahmassebi and Haroldo V. Ribeiro. COVID-19 daily evolution of propagation: regional preference and persistence, variance structures, “super-spreaders” and “super-isolators”
• UN75+1 (Online, November 2021). Golnaz Shahtahmassebi, Jack Sutton and Quentin Hanley. Bayesian inference for risk comparison with application to COVID-19 mortality rate in the UK
• NetSci2019 (USA Vermont, May 2019). Jack Sutton, Golnaz Shahtahmassebi, Haroldo V. Ribeiro and Quentin Hanley. Can we see two types of crime based on connections to mortality and property. Are Networks the Way Forward?
• 2019 EUROCRIM (Belgium Ghent, September 2019). Jack Sutton, Golnaz Shahtahmassebi, Haroldo V. Ribeiro and Quentin Hanley. What is Crime attached to: Crime, Mortality and Property after removing per capita bias

Recent publications

• Mohammed, A., Shahtahmassebi, G., Ribeiro, H. v., Sutton, J., & Hanley, Q. S., Nonlinear Rank Scaling and Hidden Structure in NHS Expenditure Transparency Data. ArXiv 2025. https://doi.org/https://doi.org/10.48550/arXiv.2506.19520
• Loureiro, N.A., Neto, C.R., Sutton, J. et al. Impact of inter-city interactions on disease scaling. Sci Rep 2025;15:498. https://doi.org/10.1038/s41598-024-84252-z
• Yu, H. Q., Sutton, J., O’Neill, S., & Reiff-Marganiec, S. Case Studies on LLM Centric and Services Oriented Data Analytics Agent Development. Proceedings of the 2024 13th International Conference on Software and Information Engineering, 69–76. https://doi.org/10.1145/3708635.3708655
• Ribeiro, H. v., Sutton, J., & Hanley, Q. S. (2024). Density scaling laws and rural-to-urban transitions. In Urban Scaling (pp. 98–110). Routledge.
• Sutton J, Shahtahmassebi G, Hanley Q.S., Ribeiro H.V., A heteroscedastic Bayesian generalized logistic regression model with application to scaling problems. Chaos, Solitons & Fractals 2024;182:114787. https://doi.org/10.1016/j.chaos.2024.114787
• Sutton J, Shahtahmassebi G, Ribeiro H.V., Hanley Q.S., Population density and spreading of COVID-19 in England and Wales. PLoS One 2022;17:e0261725. https://doi.org/10.1371/journal.pone.0261725
• Ribeiro H.V., Sunahara A.S., Sutton J, Perc M, Hanley Q.S., City size and the spreading of COVID-19 in Brazil. PLoS One 2020;15:e0239699. https://doi.org/10.1371/journal.pone.0239699
• Sutton J, Shahtahmassebi G, Ribeiro H.V., Hanley Q.S., Rural–urban scaling of age, mortality, crime and property reveals a loss of expected self-similar behaviour. Sci Rep 2020;10:16863. https://doi.org/10.1038/s41598-020-74015-x