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    Dr Eben Afrifa-Yamoah

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Dr Eben Afrifa-Yamoah

Senior Lecturer

Staff Member Details
Telephone: +61 8 6304 2133
Email: e.afrifayamoah@ecu.edu.au
Campus: Joondalup
ORCID iD: https://orcid.org/0000-0003-1741-9249

Overview of role

Eben is a Senior Lecturer in Data Science, and the unit set coordinator for Data Science in the School of Science. He also co-leads the Mathematical Applications and Data Analytics (MADA) research group.

Current Teaching

  • MAT2440 Time Series Forecasting
  • MAT3110 Applied Multivariate Statistics
  • RES5115 Research Preparation: Principles and Approaches
  • MAT6100 Time Series Forecasting

Background

Dr. Ebenezer Afrifa-Yamoah is a statistical data scientist who provides broad consultation on multidisciplinary research projects. He develops innovative statistical methods for analysing complex, high-dimensional data sets, particularly in medical and environmental sciences. His research integrates advanced techniques in high-dimensional data analysis, machine learning, causal inference, spatiotemporal modeling, and psychometrics to address multidisciplinary challenges.

Professional Memberships

  • 2021 - Statistical Society of Australia (SSA), Member
  • 2019 - Computational and Methodological Statistics, Member
  • 2019 - Statistical Modelling Society, Member

Awards and Recognition

  • 2025 – Finalist, Premier’s Science Awards (Early Career Scientist of the Year), Western Australian Government, Western Australia
  • 2025 - Nominee, VC Awards for Research Excellence (Early Career Researcher) Edith Cowan University, Perth, Australia.
  • 2024 - Nominee, VC Awards for Research Excellence (Early Career Researcher) Edith Cowan University, Perth, Australia.
  • 2017 – Western Australia Department of Fisheries Research Agreement Scholarship, Edith Cowan University, Perth, Australia.
  • 2014 – Quota Scheme Scholarship, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

Research Areas and Interests

  • Statistics
  • Spatiotemporal modelling
  • Time Series forecasting
  • Statistical modelling – both frequentist & Bayesian paradigms
  • Machine learning
  • Multidisciplinary
  • Spatial statistics

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