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    [세미나] [ISE Colloquium] Nov 27 16:00 / E2 1501 / Nonparametric Statistical Decision Making / Prof. Hoseung Song / KAIST
    • 관리자
    • 2024.11.20
    • 48
    ISE 콜로퀴움이 다음과 같이 진행될 예정입니다.
     
    # 날짜/시간: 2024년 11월 27일 수요일 16:00~17:00
    # 장소: E2 1501
    # 연사:  Prof. Hoseung Song / KAIST
    # 제목:  Nonparametric Statistical  Decision Making
     
    # 초록 : 

    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.

    #Bio 

    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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    ISE dept. invites you to the following seminar.

     

    # Time/Date : Nov. 27, 2024 (Wend.) 16:00~17:00

    # Location: E2 1501

    Presenter: Prof. Hoseung Song / KAIST

    Title:   Nonparametric Statistical  Decision Making

     

     
    # Abstract : 

    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.

    #Bio 

    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.