In this talk, I will briefly introduce how statistics is used in hypothesis testing and decision making. Statistics plays a crucial role in data analysis and inference, helping to draw conclusions from the data  based on probability and mathematical principles. In this era of big data, complex and high-dimensional data are ubiquitous in various scientific fields. This talk will focus on nonparametric approaches, and two ongoing projects will be introduced: spatial clustering tests and online change-point detection.

Hoseung Song is an assistant professor in the Department of Industrial and Systems Engineering at KAIST. Previously, he worked as a postdoctoral researcher at Fred Hutch and obtained his Ph.D. in Statistics from UC Davis. His research focuses on developing practical statistical tools for analyzing high-dimensional and large-scale data.

 

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# Location: E2 1501

# Presenter: Prof. Hoseung Song / KAIST

 

In this talk, I will briefly introduce how statistics is used in hypothesis testing and decision making. Statistics plays a crucial role in data analysis and inference, helping to draw conclusions from the data  based on probability and mathematical principles. In this era of big data, complex and high-dimensional data are ubiquitous in various scientific fields. This talk will focus on nonparametric approaches, and two ongoing projects will be introduced: spatial clustering tests and online change-point detection.

Hoseung Song is an assistant professor in the Department of Industrial and Systems Engineering at KAIST. Previously, he worked as a postdoctoral researcher at Fred Hutch and obtained his Ph.D. in Statistics from UC Davis. His research focuses on developing practical statistical tools for analyzing high-dimensional and large-scale data.