About Ying Sun Ying Sun Professor (former), Statistics spatio-temporal statistics environmental applications computational statistics Professor Ying Sun leads a research group focusing on environmental statistics. She is at the forefront of developing advanced models and methodologies in the fields of spatio-temporal statistics, functional data analysis and process monitoring. Events Presented Events Nov 24 - Nov 30, 2024 Spatio-Temporal Statistics in Geo-Environmental Data Science Ying Sun, Professor (former), Statistics Nov 26, 15:00 - 16:30 B9, L2, R2322 spatio-temporal statistics In this talk, I will discuss the contributions and ongoing research of my Environmental Statistics Research Group in the area of spatio-temporal statistics, with a particular focus on geo-environmental data science. Our work is primarily centered around the development and application of sophisticated statistical models that improve the understanding and management of environmental data characterized by their spatial and temporal variability. My group has made significant advances in developing better spatio-temporal models that effectively capture the complexities inherent in environmental datasets, as well as developing innovative software tools such as ExaGeoStat, ParallelVecchiaGP, and DeepKriging, which support the analysis of large-scale geostatistical datasets. During this presentation, I will also showcase our research contributions motivated by environmental applications, including multivariate time series visualization and clustering, panel data analysis for functional and spatial data, and statistical process monitoring.
Spatio-Temporal Statistics in Geo-Environmental Data Science Ying Sun, Professor (former), Statistics Nov 26, 15:00 - 16:30 B9, L2, R2322 spatio-temporal statistics In this talk, I will discuss the contributions and ongoing research of my Environmental Statistics Research Group in the area of spatio-temporal statistics, with a particular focus on geo-environmental data science. Our work is primarily centered around the development and application of sophisticated statistical models that improve the understanding and management of environmental data characterized by their spatial and temporal variability. My group has made significant advances in developing better spatio-temporal models that effectively capture the complexities inherent in environmental datasets, as well as developing innovative software tools such as ExaGeoStat, ParallelVecchiaGP, and DeepKriging, which support the analysis of large-scale geostatistical datasets. During this presentation, I will also showcase our research contributions motivated by environmental applications, including multivariate time series visualization and clustering, panel data analysis for functional and spatial data, and statistical process monitoring.
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