Azizur Rahman

Professor Azizur Rahman

Statistician and Data Scientist

Computing, Mathematics and Engineering

Biography

Professor Azizur Rahman is an applied statistician and data scientist with expertise in both developing and applying novel methodologies, models and technologies. He is able to assist in understanding multi-disciplinary research issues in various fields with the interaction or adaptation of statistics, data science, AI, and ML. In particular, his research solve significant applied questions, including how to understand the individual activities which occur within very complex behavioural, socio-economic and ecological systems.

Prof. Rahman develops data-centric "alternative computational methods in microsimulation modelling technologies", which are handy tools for decision-making processes in government and non-government organizations, precision estimation, policy analysis and evaluation. He has accrued more than $4.03 million of external research funding and over 215 scholarly publications and received several awards, including the ANZRSAI's 2023 Outstanding Service Award and the Charles Sturt Excellence Awards in 2023 and 2024. He also ranked as one of the world's top 2% researchers by the Stanford University/Elsevier rankings.

Prof. Rahman is the leader of “Data Mining Research Group” and founded the 'Data Analytics Lab' at Charles Sturt.”

Research
  • Bayesian inference, multilevel modelling and big-data analysis
  • Model validation with uncertainty or reliability estimates
  • Socioeconomic, demographic and health research
  • Spatial analysis and small area estimation
  • Statistics, data mining and deep learning
Publications
Full publications list on CRO

Recent publications

  • Parween, S., Olbert, A. I., Bamal, A., Sajib, A. M., Diganta, M. T. M., Hasan, M. A., Ahmed, Y., Moniruzzaman, M., Rahman, A., & Uddin, M. G. (2026). Advancing groundwater quality assessment in Siliguri City of India through the RMS-WQI model incorporating the data-driven approaches. City and Environment Interactions, 29, Article 100270. https://doi.org/10.1016/j.cacint.2025.100270
  • Hossain, M. M., Abdulla, F., & Rahman, A. (Eds.) (Accepted/In press). Modelling Climate Change Impacts: Data Science, AI and Machine Learning Approaches. (1 ed.) John Wiley & Sons.
  • Gregoric, C., McLeod, S., Hopf, S. C., Downey, B., Rahman, A., Sikder, S., Zischke, C., Tran, V. H., Murray, E., McAlister, H., Ivory, N., Delli-Pizzi, L., Elwick, S., Dealtry, L., & Davies, J. (2025). Advancing the Sustainable Development Goals by listening to children's voices across the globe. Child Language Teaching and Therapy. https://doi.org/10.1177/02656590251406102
  • Wood, N., Rahman, A., Ip, R. H. L., & Graham, J. (2025). Assessment of non-destructive nuclear and non-nuclear asphalt density testing devices for Australian road construction. Nondestructive Testing and Evaluation, 40(10), 4696-4716. Article 2427343. https://doi.org/10.1080/10589759.2024.2427343
  • Tithi, S. K., Kuddus, M. A., Rahman, A., & Hoque, A. (2025). Mathematical modelling and bifurcation analysis of human-mosquito interactions for malaria control in Bangladesh. Bradleya, 43(5), 197-226. https://doi.org/10.61586/reNdh