Xiang Ye

PhD Candidate in Bayesian computational statistics

Email

GitHub

Scholar

“志存高远，脚踏实地。”
“Aim high, stay grounded.”

“行百里者半于九十。”
“The last mile is the longest.”

I am a PhD candidate in the Bayesian Computational Statistics and Modeling Research Group at King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue.

My PhD research focuses on Bayesian latent variable models, spanning prior specification and model construction, and their applications. During my PhD, I have developed these ideas in the context of directional (circular) statistics.

I am seeking postdoctoral and research positions. Please feel free to contact me at any time.

Education

  • PhD in Statistics | King Abdullah University of Science and Technology, Saudi Arabia (2023-present)
  • MSc in Statistics | Lancaster University, UK (2021-2022)
  • BSc in Applied Mathematics | University of Liverpool & Xi’an Jiaotong‑Liverpool University, Suzhou, China (2017-2021)

Research Interests

My research interests center on efficient, tractable, and explainable approaches to statistical inference and learning from data. I am especially interested in the synergy between Bayesian inference and modern high-dimensional learning frameworks to develop methodologies for real-world problems.

  • Scalable probabilistic modeling – Using structured latent representations and hierarchical Bayesian models to handle high-dimensional data
  • Uncertainty quantification – Developing frameworks to estimate predictive uncertainty and improve model calibration in complex systems
  • Prior specification – Designing principled frameworks for prior construction to improve model stability and inferential accuracy
  • Bayesian deep learning - Integrating Bayesian principles with deep neural networks to develop accurate, efficient, and statistically robust learning methods
  • Directional statistics – developing methodology for statistical models involving variables defined on manifold-valued spaces (e.g. circular, spherical models)

Publications

  • Ye, X., Van Niekerk, J. and Rue, H., 2025. Principled priors for Bayesian inference of circular models. arXiv preprint arXiv:2502.18223.

Award

  • Master Dissertation Award: Department Prize for Best Computational Dissertation for Spatial Statistical Modeling with INLA, 2022
  • Meritorious Winner & Student Advisor, Mathematical Contest in Modeling (MCM), 2020
    • Analyzed product sales data and customer reviews to identify patterns and forecast demand.
    • Developed predictive models to support strategic recommendations for Amazon market entry.
  • Excellent Team Member of ENACTUS, 2017-2019
    • Helping to protect and maintain the traditional legacy of nut‑cutting handicraft through business operating mode.

Intern and Research Experience

  • Research and Development Intern | Tech View Info Limited Liability Company, Guangzhou, China (2023)
  • Project Marketing Intern | Guangdong Zhujiang Investment CO LTD, Guangzhou, China (2019)

Skills

  • Programming: R (INLA, Stan, ggplot2, caret, keras…), Python, C++, MATLAB
  • Tools & Software: LaTeX, Microsoft Office, Adobe Photoshop, Adobe Premiere Pro
  • Languages:
    • English (Professional proficiency; IELTS 7.0, 2021)
    • Chinese (Mandarin & Cantonese; native proficiency)
    • German (A2)