Rate My Professor Hanlin Shang

Shang's research specializations encompass functional data analysis, Bayesian econometrics, computational statistics, and demographic forecasting, with applications to mortality and fertility rates, financial time series, and high-dimensional modeling. Notable publications include 'Detection and estimation of structural breaks in high-dimensional functional time series' (Annals of Statistics, 2024), 'Forecasting Australian fertility by age, region, and birthplace' (International Journal of Forecasting, 2024), 'Long-range dependent curve time series' (Journal of the American Statistical Association, 2020), and 'Nonstationary functional time series forecasting' (Journal of Forecasting, 2025). He has secured significant funding, including the ARC Future Fellowship FT240100338 (2025-2028) for 'Functional Panel Data Analysis: Harnessing Big Data for Society', ARC Discovery Project DP230102250 (2022) for 'Feature Learning for High-dimensional Functional Time Series', ARC Discovery Project DP170102468 (2017), and various Macquarie University grants such as the Research Accelerator Grant Scheme (2020) and Data Horizons Projects (2023-2024). Shang has extensive visiting scholar experience at institutions including Columbia University, Cornell University, City University of London, and the United Nations Population Division. He serves as Editor of the Australian and New Zealand Journal of Statistics and Associate Editor for the Journal of Computational and Graphical Statistics, International Journal of Forecasting, Demographic Research, and others. Additionally, he has supervised multiple PhD students and postdocs, several of whom now hold lectureships at universities like Monash, ANU, and the University of Newcastle.