Xiaotian's research focuses on developing computationally efficient non-Gaussian statistical frameworks and the theoretical analysis, with a particular focus on modeling complex spatio-temporal dependencies in high-dimensional environmental datasets.
Biography
Xiaotian Jin is a Ph.D. candidate in Statistics at King Abdullah University of Science and Technology (KAUST), working under the supervision of Professor David Bolin. He received his B.S. degree in computer science from Wenzhou-Kean University, China, in 2020 and his M.S. degree in Statistics from KAUST in 2021.
Research Interests
Xiaotian’s research focuses on the intersection of computational statistics, geostatistics theory. He specializes in developing unified, computationally efficient frameworks for modeling non-Gaussian data, addressing the limitations of traditional Gaussian-based systems in capturing complex, real-world data characteristics.
His work spans the mathematical analysis of convergence properties in sampling algorithms, the design of hierarchical spatio-temporal models, and the application of these methods to high-dimensional environmental datasets, such as global oceanographic profiles.
Qualifications
Education
- Master of Science (M.S.)
- Statistics, King Abdullah University of Science and Technology (KAUST), Saudi Arabia, 2021
- Bachelor of Science (B.S.)
- Computer Science, Wenzhou-Kean University (WKU), China, 2020
