About Me
I’m Harry, a final-year PhD student at the University of St Andrews, where I study Bayesian statistics.
My research is focused on Bayesian nonparametric methods for clustering analysis. I work primarily with Bayesian mixture models, especially Dirichlet Process Mixture Models, to detect meaningful clusters in complex data. A key aspect of my work is uncertainty quantification, both in terms of how individual data points are assigned to clusters and how the overall clustering structure can vary under uncertainty.
I am also interested in computation. I work on distributed and parallel approaches for large-scale Bayesian inference, with the goal of making flexible Bayesian models practical for modern datasets that are large, high-dimensional, or computationally demanding.
Outside of my core research, I enjoy learning about machine learning and AI more broadly. I am particularly interested in how Bayesian thinking, probabilistic modeling, and uncertainty awareness can interact with and enrich modern machine learning methods.
Education
University of St Andrews (Sept. 2022 - Present)
- Doctor’s Degree in Statistics (Ph.D.)
- Supervised by： Dr. Michail Papathomas & Dr. Nicolò Margaritella
University of Southampton (Sept. 2020 - Dec. 2021)
- MSc., Data and Decision Analytics
- Supervised by：Dr. Alain B. Zemkoho & Dr. Emmanuel Kagning-Tsinda
Southwest Jiaotong University (Sept. 2016 - Jun. 2020)
- BSc., Mathematics and Applied Mathematics
- Supervised by：Dr. Hua Meng (孟华)
Sichuan University (Sept. 2017 - Aug. 2018)
- One-year exchange programme in the School of Mathematics
If I feel unhappy, I do mathematics to become happy. If I am happy, I do mathematics to keep happy.