Internship on Deep learning Methods for Satellite Data Downscaling
ApplyProject Description
The student will learn state-of-the-art statistical and machine learning methods for image super-resolution, explore various sources of satelliteimages and deploy experiments to manipulate and analyze large scale dataset.
Program -
Statistics
Division -
Computer, Electrical and Mathematical Sciences and Engineering
Field of Study -
Statistics, Data Science, Environmental Science, Computer Science,Applied Mathematics
About the
Researcher
Ying Sun
Associate Professor, Statistics
Ying Sun is an Assistant Professor of Statistics in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE). She joined KAUST after one-year service as an assistant professor in the Department of Statistics at the Ohio State University.
Before joining the Ohio State University, she was a postdoctorate researcher at the University of Chicago in the research network for Statistical Methods for Atmospheric and Oceanic Sciences (STATMOS), and at the Statistical and Applied Mathematical Sciences Institute (SAMSI) in the Uncertainty Quantification program.
Her research interests include spatio-temporal statistics with environmental applications, computational methods for large datasets, uncertainty quantification and visualization, functional data analysis, robust statistics, statistics of extremes.
Before joining the Ohio State University, she was a postdoctorate researcher at the University of Chicago in the research network for Statistical Methods for Atmospheric and Oceanic Sciences (STATMOS), and at the Statistical and Applied Mathematical Sciences Institute (SAMSI) in the Uncertainty Quantification program.
Her research interests include spatio-temporal statistics with environmental applications, computational methods for large datasets, uncertainty quantification and visualization, functional data analysis, robust statistics, statistics of extremes.