Rate My Professor Han Gan

Dr. Han Gan is a Senior Lecturer in the Department of Mathematics at the University of Waikato, School of Computing and Mathematical Sciences, where he serves as Programme Leader for Data Analytics. He earned his PhD in Mathematics from the University of Melbourne in 2014, with a thesis titled 'Conditional Distribution Approximation and Stein's Method' supervised by Aihua Xia and Kostya Borovkov. Prior to his doctorate, he completed a Bachelor of Commerce and Bachelor of Science (Honours) at the University of Melbourne. Gan joined the University of Waikato in January 2020, advancing to Senior Lecturer, and teaches primarily in the Data Analytics programme while supervising Masters and PhD students.

Gan's research focuses on probability theory, utilizing Stein's method to develop approximations for equilibrium distributions in stochastic models including Cannings models with mutation, Wright-Fisher models, Poisson-Dirichlet processes, and two-island seed-bank models, with applications to genomics and random permutations. Key publications include 'Dirichlet approximation of equilibrium distributions in Cannings models with mutation' (Advances in Applied Probability, 2017), 'Stein factors for negative binomial approximation in Wasserstein distance' (2015), 'Stein’s method for the Poisson–Dirichlet distribution and the Ewens sampling formula, with applications to Wright–Fisher models' (2021), and 'Arcsine laws for random walks generated from random permutations with applications to genomics' (Journal of Applied Probability, 2021). He contributes to interdisciplinary health research, including COVID-19 inequities modeling, diabetes care impact studies, and evaluations of continuous glucose monitoring equity, supported by Health Research Council of New Zealand funding. Gan leads statistical consulting via Te Ara Tatauranga and organizes sessions on probability and mathematical statistics at international conferences.