SCoDA Workshop: Statistics on Complex Data - Seoul 2026

Session

Young Researcher Session

26 Jun 2026, 15:50

Seoul National University

Conveners

Young Researcher Session

• KAZUYOSHI YATA (U. Tsukuba)

15. Inference on High-Dimensional Mean Vectors and Their Applications via Automatic Sparse Estimation Methodology

Tetsuya Umino (Degree Programs in Pure and Applied Sciences, Graduate School of Science and Technology, University of Tsukuba, Ibaraki, Japan)

26/06/2026, 15:50

High-dimensional, low-sample-size (HDLSS) data are now common in modern scientific fields, such as genomics and medical imaging, where the number of variables significantly exceeds the number of observations. In this talk, we consider the estimation of linear combinations of high-dimensional mean vectors in multi-sample settings. Such inference is a fundamental component of various...

17. Generalized Frechet mean with random minimizing domain

Jaesung Park (Seoul National University)

26/06/2026, 16:15

This paper introduces a novel extension of Fréchet means, referred to as generalized Fréchet means, as a comprehensive framework for describing the characteristics of random elements. The generalized Fréchet mean is defined as the minimizer of a cost function, and the framework encompasses various extensions of Fréchet means that have appeared in the literature. The most distinctive feature of...

22. Data Analysis with Information Centrality

Siwon Kim (Seoul National University)

26/06/2026, 16:40

Information centrality is a vertex measure of a graph, which is a harmonic mean of effective conductances defined between a given vertex and all other connected vertices. As the reciprocal of effective resistance, effective conductance serves as a higher-order connectivity measure and has a connection with a renowned mathematical data analysis technique, spectral embedding, by the graph...

24. Linking Cancer and Density Representations in Mammography via AJIVE and Explainability Methods

Jan Zavadil (UiT)

26/06/2026, 17:05

Mammography screening produces multiple images per patient, each assessed for cancer risk and breast density using the BI-RADS system. While these tasks are clinically related, density both increases cancer risk and reduces imaging sensitivity, multi-task learning models do not consistently outperform single-task baselines. This raises a natural question: how much information is truly shared...