Rate My Professor Aloke Phatak

Aloke Phatak, Adjunct Professor in the Faculty of Science and Engineering at Curtin University, is an accomplished applied statistician with a distinguished career spanning academia, government research, and industry collaboration. He earned his BASc, MASc, and PhD from the University of Waterloo in Canada, transitioning from engineering—initially in rocket science—to statistics. For over two decades at CSIRO, Phatak advanced applied statistics across sectors, focusing on industrial statistics, biomarker discovery, climate extremes modeling, and more. He developed and delivered short courses tailored for engineers and scientists, enhancing practical statistical literacy in professional settings.

Since joining Curtin University in 2015 as a Senior Lecturer in the School of Electrical Engineering, Computing and Mathematical Sciences, Phatak has significantly shaped data science education and research. He spearheaded the creation of the undergraduate Data Science major, currently coordinates the program, and conducts industry consulting. As Associate Director of the ARC Training Centre for Transforming Maintenance through Data Science, he fosters interdisciplinary training and innovation. Additionally, his adjunct role with the Curtin Institute of Radio Astronomy underscores his contributions to astronomical data analysis.

Phatak's research portfolio is diverse, encompassing Bayesian methods, chemometrics, climate research, statistical pedagogy, mineral prospectivity analysis, machine learning applications in health risk prediction, and transient detection in large astronomical surveys. Notable publications include: Stephenson, A. G., Lehmann, E. A., and Phatak, A. (2016). "A max-stable process model for rainfall extremes at different accumulation durations." Weather and Climate Extremes, 13, 44-53; Lehmann, E. A., Phatak, A., Stephenson, A., and Lau, R. (2016). "Spatial modelling framework for the characterisation of rainfall extremes at different durations and under climate change." Environmetrics, 27(4), 239-251; Malacova, E. et al. (2020). "Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia 1980–2015." Scientific Reports, 10, 5085; Fu, S. C. et al. (2025). "New Metrics for Identifying Variables and Transients in Large Astronomical Surveys." The Astrophysical Journal, 976(2), 149. His work supports real-world problem-solving through rigorous statistical approaches and has influenced fields from environmental modeling to public health and astronomy.