IMI Colloquium

2024年7月　IMI Colloquium
　
■日時：　 7月10日(水) 16:45 - 17:45
■場所：　 IMIオーディトリアム及びZoomによるオンラインコロキウム
■使用言語：英語
■講師：　 GIM Minjung氏
            National Institute for Mathematical Sciences, Korea
           Ajou University Department of Mathematics, Korea
            Soon-Sun Kwon氏      
　　　　　       Ajou University Department of Mathematics, Korea
■講演タイトル
   GIM Minjung氏： Statistical Distances with Mathematical Explanation
　 Soon-Sun Kwon氏： Statistical Learning Models in Functional structure of Clinical data
■講演要旨
Prof. GIM, Minjung (National Institute for Mathematical Sciences/Ajou University)
　　"Statistical Distances with Mathematical Explanation”

Statistical distances measure the difference between distributions or data samples and are employed in various
machine learning applications. In this talk, I will introduce several statistical distances and review their mathematical
interpretations. We will demonstrate how to use SciPy's statistical distance functions.
Using visual illustrations, we will describe the inner workings and properties of several common statistical distances,
explaining what makes them both convenient to use and powerful for solving various problems. Additionally, we will
present real-life applications and concrete examples.

Prof. KWON, Soon-Sun (Ajou University)
“Statistical Learning Models in Functional structure of Clinical data”

In this talk, I introduce two topics about longitudinal data analysis and gait data analysis.
First, longitudinal data are used in statistical studies that accept many repeated measurements as well as
the different time spans of the measurements between or within subjects. Furthermore, correct inferences
can particularly be obtained by considering the correlation between repeated measurements within subjects.
Under the assumption, I propose the clustering method using the Fr'echet distance for multi-dimensional
functional data. And I apply the sparse clustering method to multi-dimensional thyroid cancer data collected
in South Korea. Second, motivated by gait data from both the normal and the cerebral palsy (CP) patients
group with various gross motor function classification system (GMFCS) levels, I propose a multivariate functional
classification method to investigate the relationship between kinematic gait measures and GMFCS levels.
The method is generalized to handle multivariate functional data and multi-class classification. The method
yields superior prediction accuracy and provides easily interpretable discriminant functions.

開催報告: https://www.imi.kyushu-u.ac.jp/post-10445/
・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・・
IMI Colloquium in July 2024
＊If you are interested in the colloquium, please register here. Registration deadline is Wednesday, July 3.
      https://forms.office.com/r/qRystXZkim

■Date : Wednesday, 10 July 2024 16:45-17:45
■Place : IMI Auditorium(W1-D-413) and Live streaming with ZOOM
■Language：English
■Speaker : Prof. GIM Minjung
               National Institute for Mathematical Sciences/Ajou University Department of Mathematics, Korea
            Prof. Soon-Sun Kwon
              Ajou University Department of Mathematics, Korea
■Title
Prof. GIM Minjung： Statistical Distances with Mathematical Explanation
　 Prof. Soon-Sun Kwon： Statistical Learning Models in Functional structure of Clinical data
■Abstract
Prof. GIM, Minjung (National Institute for Mathematical Sciences/Ajou University)
　　"Statistical Distances with Mathematical Explanation”

Statistical distances measure the difference between distributions or data samples and are employed in various
machine learning applications. In this talk, I will introduce several statistical distances and review their mathematical
interpretations. We will demonstrate how to use SciPy's statistical distance functions.
Using visual illustrations, we will describe the inner workings and properties of several common statistical distances,
explaining what makes them both convenient to use and powerful for solving various problems. Additionally, we will
present real-life applications and concrete examples.

Prof. KWON, Soon-Sun (Ajou University)
“Statistical Learning Models in Functional structure of Clinical data”

In this talk, I introduce two topics about longitudinal data analysis and gait data analysis.
First, longitudinal data are used in statistical studies that accept many repeated measurements as well as
the different time spans of the measurements between or within subjects. Furthermore, correct inferences
can particularly be obtained by considering the correlation between repeated measurements within subjects.
Under the assumption, I propose the clustering method using the Fr'echet distance for multi-dimensional
functional data. And I apply the sparse clustering method to multi-dimensional thyroid cancer data collected
in South Korea. Second, motivated by gait data from both the normal and the cerebral palsy (CP) patients
group with various gross motor function classification system (GMFCS) levels, I propose a multivariate functional
classification method to investigate the relationship between kinematic gait measures and GMFCS levels.
The method is generalized to handle multivariate functional data and multi-class classification. The method
yields superior prediction accuracy and provides easily interpretable discriminant functions.

report: https://www.imi.kyushu-u.ac.jp/post-10451/