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 in a range of applications including scaling, health and crime.

Teaching responsibilities

I'm the module leader for all the statistics modules in our undergraduate Artificial Intelligence and Data Science portfolio. This includes the modules: Statistical Methods (first year), Advanced Statistics (second year), and Stochastic Processes (third year).

Research interests

I am interested in all aspects of Statistics and Data Science. My recent research has involved advancing statistical methodologies to address complex challenges in large data-intensive problems of scale and uncertainty. My research interests are mainly in the following:

• Bayesian Inference
• Linear, Generalised Linear and Generalised Mixed Modelling
• Computation Statistics
• Spatial and Spatial-Temporal Modelling
• Linear and Non-linear Data Problems
• Application in Health, Crime, Property Transactions and Disease

Membership of professional bodies

• Member of the Institute of Mathematical Statistics
• Member of the Royal Statistical Society
• Awarded a Fellowship by the Higher Education Academy (FHEA) in 2023

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

• 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

Experience in industry

During my time at the University of Derby, I have formed strong collaborative relationships with partners within the NHS. This collaboration aims to enhance service provision, planning and design, and provide precise targeting information to efficiently allocate health and social care resources.

Recent publications

• Sutton, J., Shahtahmassebi, G., Hanley, Q. S., and Ribeiro, H. V. (2023), “A Heteroscedastic Bayesian Generalized Logistic Regression Model with Application to Scaling Problems,” arXiv preprint arXiv: 2311.17808
• Sutton, J. (2022), “Allometric and Fluctuation Scaling in Health, Well-Being and Mortality,” Doctoral dissertation, Nottingham Trent University
• Sutton, J., Shahtahmassebi, G., Ribeiro, H. V., and Hanley, Q. S. (2022), “Population density and spreading of COVID-19 in England and Wales,” PLOS ONE, (J. S. Andrade, ed.), 17, e0261725. https://doi.org/10.1371/journal.pone.0261725
• Ribeiro, H. V., Sunahara, A. S., Sutton, J., Perc, M., and Hanley, Q. S. (2020), “City size and the spreading of COVID-19 in Brazil,” PLOS ONE, (L.-L. Jiang, ed.), 15, e0239699. https://doi.org/10.1371/journal.pone.0239699
• Sutton, J., Shahtahmassebi, G., Ribeiro, H. V., and Hanley, Q. S. (2020), “Rural–urban scaling of age, mortality, crime and property reveals a loss of expected self-similar behaviour,” Scientific Reports, 10. https://doi.org/10.1038/s41598-020-74015-x