Xiran Zhang
Xiran Zhang research investigates statistics and high-performance computing, with a particular focus on scalable methods for large-scale geostatistical and spatio-temporal problems.
Biography
Xiran Zhang is a Ph.D. candidate in Statistics at King Abdullah University of Science and Technology (KAUST). He received his B.S. in Mathematics and Applied Mathematics from the University of Science and Technology of China (USTC) in June 2021 and his M.S. in Statistics from KAUST in December 2022. His research lies at the intersection of statistics and high-performance computing, with a particular focus on scalable methods for large-scale geostatistical and spatio-temporal problems. Key words of his work include distributed CPU/GPU computing, parallel algorithms, uncertainty quantification for massive spatial data, and spatio-temporal cross-covariance modeling.
Xiran has his work published or presented at major international conferences, including IPDPS, JSM, and SC. In addition to his research, he has been actively involved in teaching and mentoring, serving as a teaching assistant for several STAT courses at KAUST and at King Fahad Security College for the Ministry of Interior. He has received several honors, including the Al-Kindi Statistics Top Quals Student Award in 2021 and the KAUST Dean’s List Award in 2024 and 2025.
Research Interests
During his doctoral studies, he has developed high-performance computational frameworks for credible and confidence region detection in massive geostatistical datasets, designed optimized implementations on distributed runtime systems such as PaRSEC and StarPU, and worked on GPU-accelerated scientific computing pipelines. He has also contributed to task-based parallel computing for statistical software through RCOMPSs, an open-source runtime system for R, and has collaborated with international research teams including the Barcelona Supercomputing Center and the University of Colorado Denver.
Related People
Academic Advisors
Marc G. Genton
- Al-Khawarzmi Distinguished Professor, Statistics