Rate My Professor Xun Xiao

Dr. Xun Xiao is a Senior Lecturer in the Department of Mathematics and Statistics at the University of Otago. He holds a B.S. in Statistics from the University of Science and Technology of China in 2011 and a Ph.D. from the Department of Systems Engineering and Engineering Management at City University of Hong Kong in 2016. Prior to his current position, he served as a Research Assistant at City University of Hong Kong from October 2015 to July 2016. He joined the University of Otago as a Lecturer in Statistics in June 2021 and progressed to Senior Lecturer. Dr. Xiao is the Director of Studies for 100-level Statistics and teaches STAT110 Statistical Methods, an introductory course accessible to students from Sciences, Health, Business, Humanities, and other fields. Through his teaching, he promotes statistical thinking to extract meaningful insights from noisy data, with applications in industrial systems, transportation, veterinary science, food science, and natural hazards.

Dr. Xiao's academic interests focus on data analytics in industrial and related areas, reliability and survival analysis, statistical quality control, statistical process control, and point process modeling. His research in industrial engineering employs mathematical, statistical, scientific, and engineering principles to model and optimize complex systems. As Principal Investigator of a 2024 Marsden Fund Fast-Start Grant, he advances statistical methods to address uncertainty in complex infrastructure asset networks. He has also contributed as co-investigator to three projects funded by the National Natural Science Foundation of China. With over 30 peer-reviewed journal articles in engineering, statistics, and energy, notable publications include "Physics-informed condition monitoring for wind turbines via change point detection under heteroscedasticity" (Reliability Engineering & System Safety, 2026), "Modeling frequent event recurrence in manufacturing logs with Markov-modulated renewal processes" (IEEE Transactions on Knowledge & Data Engineering, 2026), "Optimal maintenance planning for mission-oriented systems considering dynamic mission duration" (Naval Research Logistics, 2026), "TFTformer: A novel transformer based model for short-term load forecasting" (2025), and "An efficient calibration algorithm for multiscale geographically and temporally weighted regression" (International Journal of Geographical Information Science, 2026). His accomplishments are recognized by the New Zealand Statistical Association’s Worsley Early Career Award in 2023 and the University of Otago Early Career Award for Distinction in Research in 2025, which includes a $5,000 research grant.