Spatio-Temporal Statistics in Geo-Environmental Data Science
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.
Overview
Abstract
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. These methods have been instrumental in addressing high-impact real-world problems. As we look to the future, our research will continue to focus on pushing the boundaries of existing statistical methodologies to provide actionable insights in response to environmental challenges.
Brief Biography
Ying Sun is an Associate Professor of Statistics at King Abdullah University of Science and Technology (KAUST). She is at the forefront of developing advanced models and methodologies in spatio-temporal statistics, functional data analysis, and process monitoring. Her scientific research has contributed greatly to environmental statistics. Professor Sun received her Ph.D. in Statistics in 2011 from Texas A&M University and accepted a postdoctorate researcher position in the Research Network for Statistical Methods for Atmospheric and Oceanic Sciences at the University of Chicago and the Statistical and Applied Mathematical Sciences Institute. She worked at the Ohio State University for one year as an Assistant Professor of Statistics before joining KAUST in 2014. She was promoted to Associate Professor in 2018. Professor Sun has won numerous awards for her research, including the Section on Statistics and the Environment Early Investigator Award from the American Statistical Association and the Abdel El-Shaarawi Young Researcher Award from the International Environmetrics Society.