Hoseung Song

Hoseung Song is an Assistant Professor at KAIST (Korea Advanced Institute of Science & Technology), affiliated with the Department of Industrial and Systems Engineering and the Graduate School of Data Science. His research focuses on statistical data science, decision making, and biomedical applications, particularly in areas like change-point analysis, two-sample tests, and spatial clustering. His work bridges theoretical statistics with practical biomedical and healthcare challenges.

Research interests include advanced statistical methodologies for analyzing complex biological and healthcare data, such as viral genomics, microbiota associations, and immune cell clustering. He develops scalable algorithms and kernel-based methods to address high-dimensional and non-Euclidean data challenges. Recent work highlights applications in infectious diseases (e.g., HSV-2) and postmenopausal health through association studies and differential analysis.

His publications emphasize robust statistical testing frameworks, including permutation-based limitations, batch effect corrections, and graph-based methodologies. These contributions enhance reliability in biomedical research and safety-critical data applications. His lab likely integrates computational statistics with real-world healthcare datasets to drive translational insights.