Azizur Rahman

Associate Professor Azizur Rahman

Statistician and Data Scientist

Computing, Mathematics and Engineering

Biography

Associate Professor Azizur Rahman is an applied statistician and data scientist with expertise in both developing and applying novel methodologies, models and technologies. Azizur is able to assist in understanding multi-disciplinary research issues within various fields including how to understand the individual activities which occur within very complex behavioural, socio-economic and ecological systems.

He develops "alternative methods in microsimulation modelling technologies" which are very useful tools to socioeconomic policy analysis and evaluation. His 2016 book has contributed significantly to the field of small area estimation and microsimulation modelling. Azizur's research interests encompass issues in simple to multi-facet analyses in various fields ranging from the mathematical sciences to the law and legal studies. He obtained the SOCM Research Excellence Award 2018 and the Charles Sturt RED Achievement Award 2019.

A/Prof Rahman leads “Statistics and Data Mining Research Group” within the Data Science Research Unit 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

Uddin, M. G., Nash, S., Diganta, M. T. M., Rahman, A., & Olbert, A. I. (2023). A Comparison of Geocomputational Models for Validating Geospatial Distribution of Water Quality Index. In Computational Statistical Methodologies and Modeling for Artificial Intelligence (pp. 243-276). CRC Press. https://doi.org/10.1201/9781003253051-16

Kuddus, M. A., Rahman, A., Alam, F., & Mohiuddin, M. (2023). Analysis of the different interventions scenario for programmatic measles control in Bangladesh: A modelling study. PLoS One, 18(6), [e0283082]. https://doi.org/10.1371/journal.pone.0283082

Uddin, M. G., Nash, S., Rahman, A., & Olbert, A. I. (2023). A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches. Water Research, 229, 1-24. [119422]. https://doi.org/10.1016/j.watres.2022.119422

Harjule, P., Rahman, A., Agarwal, B., & Tiwari, V. (2023). A Review of Computational Statistics and Artificial Intelligence Methodologies. In P. Harjule (Ed.), Computational Statistical Methodologies and Modeling for Artificial Intelligence (1 ed., Vol. 1, pp. 3-23). (Edge AI in Future Computing). Taylor & Francis. https://www.taylorfrancis.com/chapters/edit/10.1201/9781003253051-2/review-computational-statistics-artificial-intelligence-methodologies-priyanka-harjule-azizur-rahman-basant-agarwal-vinita-tiwari?context=ubx&refId=82522cea-eb82-4ebf-940c-7328ca906f1b

Tajrian, M., Rahman, A., Kabir, A., & Islam, R. (2023). A Review of Methodologies for Fake News Analysis. IEEE Access, 11, 73879.