Online Outlier Detection for Functional Data

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Project Description

The student will learn state-of-the-art statistical methods for functional data analysis and spatial statistics, explore various approaches of functional data ranking and outlier detection, especially for images and surfaces, develop algorithms for online outlier detection with applications to spatial deformation detection and brain signal analysis.​
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
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.



Desired Project Deliverables

​At the end of the internship, the student is expected to implement the designed application and submit a written report or a poster​