Hassan Ajulo ☕️
Hassan Ajulo
(he/him/his)

MPhil Candidate

James Cook University Australia

About Me

Hassan Ajulo is a final-year MPhil Candidate in the College of Medicine and Dentistry at James Cook University in Australia. His research focuses on developing statistical and machine learning methods motivated by ample information in epidemiological and biological data.

Interests
  • Spatial/Spatiotemporal Statistics
  • Bayesian Statistics
  • Causal Inference
  • Machine Learning
  • Epidemiological Modeling
  • Biological Data Analysis
Education

  • MPhil Machine Learning and Spatial Epidemiology

    James Cook University Australia

  • MSc Mathematical Sciences

    African Institute for Mathematical Sciences Rwanda

  • BSc Statistics

    University of Ilorin Nigeria

Research Statement

My current research focuses on developing machine learning (ML) and Bayesian statistical methods for applications in epidemiology. For example, I recently developed a localized spatiotemporal random forest model to study COVID-19 dynamics across US counties, capturing how epidemiological, demographic, and environmental drivers shift across regions and periods. I also collaborated in applying Bayesian frameworks, including distributed lag non-linear models, to quantify uncertainty and evaluate delayed environmental effects, such as temperature on Salmonella risk across New South Wales local health districts. These methods provide more accurate, interpretable, and actionable insights to support early interventions and inform public health decision-making.

Looking ahead, I am interested in advancing ML for applications in epidemiology and biology. As ML methods become increasingly central to these fields, the ability to move beyond correlations and rigorously establish causal effects is essential. Causal ML can help disentangle the effects of policies, vaccination campaigns, and environmental exposures on disease outcomes, as well as evaluate treatment effects on patient outcomes within the broader scope of clinical epidemiology. In biology, these approaches can also be applied to omics data, offering opportunities to better understand the causative role of genes in complex processes such as gene regulation, disease progression, and cellular development. Ultimately, my goal is to develop ML methods that advance scientific discovery while supporting more reliable and actionable decisions in public health and biomedical research.

Selected Publications
Hassan Ajulo, Faith Alele, Theophilus Emeto, Oyelola Adegboye (2025). Geospatial modelling of COVID-19 outcomes: Insights from composite indicators for pandemic preparedness (In revision).
PREPRINT
Hassan Ajulo, Theophilus Emeto, Faith Alele, Oyelola Adegboye (2025). Spatiotemporal modelling and composite indices for pandemic preparedness: A novel geospatial-temporal approach (In revision).
PREPRINT
Hassan Ajulo, Faith Alele, Theophilus Emeto, Oyelola Adegboye (2025). Spatial and spatiotemporal machine learning models for studying COVID-19 dynamics: A review of methodology and reporting practices (Revised version accepted for publication in Epidemiologic Reviews).
PREPRINT
Teaching Experience
  • Assistant Lecturer – (Nov. 2018 – Oct. 2019)
    Department of Mathematics and Statistics, The Oke-Ogun Polytechnic Saki
    Topics: Descriptive Statistics, Statistical Regression Methods, Introductory Linear Algebra, Non-Parametric Methods
Selected Academic Recognition
Technical Skills
R
Python
C
Winbugs
Latex
SQL