Professor Azizur Rahman, PhD
Charles Sturt University - Wagga Wagga Campus, Charles Sturt University, University of Chittagong, University of Canberra
0000-0002-7670-5192
About
Dr Azizur Rahman is a full Professor at the School of Computing, Mathematics and Engineering and the Leader of 'Data Mining Research Group' at Charles Sturt University, Australia. He earned a BSc (Honours) in Statistical Science, an MSc (Thesis) in Biostatistics, and a PhD in Economics and Statistics from the University of Canberra under the supervision of Professor Ann Harding, AO FASSA. He worked as a biostatistical research fellow in the Faculty of Health and Medical Sciences at the University of Adelaide. Professor Rahman is a statistician and data scientist with expertise in developing and applying novel methodologies, models, and technologies. He designs projects to understand multidisciplinary research issues within various fields with the interaction or adaptation of statistics, data science, AI, and ML. His research assists in understanding how individual and joint activities occur within very complex behavioural, socio-economic and ecological systems.
Professor Rahman develops data-centric 'alternative computational methods in microsimulation modelling technologies', which are handy tools for decision-making processes in government and nongovernmental organizations, precision estimation, policy analysis, and evaluation. He founded and runs the 'Data Analytics Lab' at Charles Sturt. Professor Rahman has accrued more than $4.03 million of external research funding and over 213 scholarly publications and received several awards, including the SOCM Research Excellence Award 2018, the Charles Sturt RED Achievement Award 2019, the ANZRSAI's 2023 Outstanding Service Award and the Charles Sturt Excellence Awards 2023 and 2024.
Employment
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Charles Sturt University - Wagga Wagga Campus Professor2012 - Present
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Charles Sturt University
Education
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University of Chittagong BSc (Honours) and MSc (Thesis)
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University of Canberra PhD
Projects & Funding
Projects & funding information is unavailable.
Publications (251)
- Leveraging bayesian models to classify myths and facts in child development Save
- Unveiling the dynamics of unemployment in Bangladesh through non-linear modeling based on economic perspective Save
- Modelling Climate Change Impacts Save
- Machine Learning for Community Well-Being: Identifying Factors Affecting Resilience in Disaster-Prone Regions Save
- Re-engineering water quality indices Save
- Feasibility of Cryptography In a Blockchain-Enhanced ICS Security Save
- Corrigendum to “A methodological framework for assessing aquatic contamination using data science approaches”(Water Resources and Industry, (2026), 35, C, (100372), (S2212371726000326), 10.1016/j.wri.2026.100372) Save
- A methodological framework for assessing aquatic contamination using data science approaches Save
- Developing river water quality prediction model incorporating reliable indexing approach Save
- Prediction to Prevention: Comparative Machine Learning Models for Early Intervention of Dactyloctenium radulans in Australia Save