Qilong Pan
Qilong Pan's research focuses on developing scalable statistical methods, with a particular emphasis on Gaussian Processes and GPU acceleration.
Biography
Qilong Pan is a Ph.D. candidate in Statistics at KAUST, supervised by Prof. Ying Sun. His research focuses on scalable Gaussian Process modeling, high-performance statistical computing, and GPU-accelerated inference for large-scale spatial and computer experiment data.
Research Interests
Qilong Pan's work bridges statistical modeling, optimization, and high-performance computing (HPC) to tackle complex challenges in geospatial analytics and machine learning. He develops efficient algorithms and software for Vecchia-based approximations, aiming to enable practical Gaussian Process applications on modern supercomputers.
Education
- Master of Science (M.S.)
- Statistics, King Abdullah University of Science and Technology (KAUST), Saudi Arabia, 2023
- Bachelor of Science (B.S.)
- Statistics, Wuhan University of Technolgy, China, 2021
- Bachelor of Arts (BA)
- English, Huazhong University of Science and Technology, China, 2021
Quote
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