About Qilong Pan Qilong Pan Ph.D. Student, Statistics statistics Deep learning spatio-temporal statistics Qilong Pan's research focuses on developing scalable statistical methods, with a particular emphasis on Gaussian Processes and GPU acceleration. Events Presented Events Nov 9 - Nov 15, 2025 Vecchia Approximations of Gaussian Processes on GPUs for Scalable Spatial Modeling and Computer Model Emulation Qilong Pan, Ph.D. Student, Statistics Nov 12, 09:00 - 11:00 B5 L5 R5209 statistics spatio-temporal statistics GPU Computing HPC This thesis advances the computational efficiency of Vecchia approximation methods for Gaussian Processes (GPs), emphasizing GPU-based implementations for large-scale geospatial analysis and computer emulation. Traditional GPs require expensive covariance matrix inversions, which this work overcomes using scalable Vecchia-based approximations without sacrificing accuracy. May 4 - May 10, 2025 Vecchia Approximations of Gaussian Processes on GPUs for Scalable Spatial Modeling and Computer Model Emulation Qilong Pan, Ph.D. Student, Statistics May 8, 12:00 - 13:00 B9 L2 R2325 machine learning Geospatial Data GPU Computing This seminar introduces GPU-accelerated Vecchia approximations to overcome Gaussian Process computational limits, enabling scalable applications for large geospatial datasets and high-dimensional computer model emulations.
Vecchia Approximations of Gaussian Processes on GPUs for Scalable Spatial Modeling and Computer Model Emulation Qilong Pan, Ph.D. Student, Statistics Nov 12, 09:00 - 11:00 B5 L5 R5209 statistics spatio-temporal statistics GPU Computing HPC This thesis advances the computational efficiency of Vecchia approximation methods for Gaussian Processes (GPs), emphasizing GPU-based implementations for large-scale geospatial analysis and computer emulation. Traditional GPs require expensive covariance matrix inversions, which this work overcomes using scalable Vecchia-based approximations without sacrificing accuracy.
Vecchia Approximations of Gaussian Processes on GPUs for Scalable Spatial Modeling and Computer Model Emulation Qilong Pan, Ph.D. Student, Statistics May 8, 12:00 - 13:00 B9 L2 R2325 machine learning Geospatial Data GPU Computing This seminar introduces GPU-accelerated Vecchia approximations to overcome Gaussian Process computational limits, enabling scalable applications for large geospatial datasets and high-dimensional computer model emulations.
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