Yang Xiao
Yang Xaio research specializes in the development of advanced spatio-temporal models and Bayesian frameworks.
Biography
Yang Xiao is a Ph.D. candidate in Statistics at King Abdullah University of Science and Technology (KAUST). With a background that bridges rigorous mathematical theory and industrial application, his work focuses on improving the accuracy of real-time predictive modeling in high-stakes environments.
Before joining KAUST, Yang spent several years as a Statistician in the pharmaceutical industry, where he specialized in experimental design, protocol development, and ensuring 100% numerical reproducibility for core research frameworks under strict regulatory standards. His academic journey began with a dual-degree background in Applied Statistics and Actuarial Science, followed by an MSc in Statistics with Data Science from the University of Edinburgh, where he focused on multi-modal signal extraction and latent pattern recognition in epidemiological data.
Research Interests
Yang’s research focuses on the intersection of Bayesian Hierarchical Modeling and high-performance algorithmic optimization. He is particularly interested in leveraging latent Gaussian processes and signal decomposition to drive superior predictive outcomes in both public health and quantitative finance.
Education
- Master of Science (M.S.)
- Statistics, The University of Edinburgh (UoE), United Kingdom, 2020
- Bachelor of Science (B.S.)
- Actuarial Science, University College Cork (UCC), Ireland, 2019
- Bachelor of Science (B.S.)
- Applied Statistics, Beijing Technology and Business University (BTBU), China, 2017