Biomedical Mathematics Group

Distribution shift in machine learning: robustness, invariance, and a causal view - Wooseok Ha
2025-12-29 16:30 - 18:30 (Asia/Seoul) | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Classical machine learning models are typically trained under the assumption that the training (source) and test (target) data are drawn from the same distribution. However, real-world data are rarely clean or consistent, and distribution shifts between the source and target domains are ubiquitous. Despite its importance, addressing distribution shifts is highly difficult. The fundamental challenge is that the problem is mathematically ill-posed: shifts can occur in many different forms, and no single method can handle all of them. While numerous algorithms have been proposed in recent years to solve distribution shifts, most are empirical-driven and lack solid foundations. In this talk, I will provide a broad overview of approaches to address distribution shift based on invariance and distributional robustness, and explain how these methods are intrinsically connected to a causal perspective. In particular, I will show why it is crucial to carefully formulate assumptions that relate the source and target domains for reliable generalization, and how assumptions grounded in the causal system enable the analysis of algorithms under both unsupervised and semi-supervised settings.

Expanding the Data Analysis Toolkit: Explainable AI, Causal Learning, and Time-Series Foundation Models - Daeil Jang
2025-12-30 15:00 - 16:00
Recent advances in data science have expanded the scope of data analysis beyond prediction accuracy toward interpretability, causal understanding, and generalizable learning across complex data structures. This lecture introduces three emerging methodological approaches that can be directly leveraged in modern data analysis workflows.
First, the lecture presents explainable artificial intelligence (XAI) techniques, focusing on SHAP and its extension to time-series explainability, to illustrate how model predictions can be decomposed into meaningful variable- and time-specific contributions. Second, it introduces machine-learning and deep-learning–based causal inference models, highlighting how these methods move beyond association to estimate intervention effects and heterogeneous impacts while maintaining interpretability. Third, the lecture explores recent time-series foundation models—such as Lag-LLaMA and TabPFN-based approaches—that enable transferable learning across diverse time-series tasks with minimal task-specific training.
Rather than treating these approaches as isolated research trends, this lecture frames them as complementary analytical tools that address key questions in data analysis: What drives model predictions? What would change under intervention? And how can models generalize across time and settings? Through this integrated perspective, the lecture aims to provide practical insight into how these three methods can be applied to real-world data analysis and inspire new research and application opportunities.

Rationalizing Therapeutics: Mathematical Insights into Drug and Cell Therapy Development - Seokjoo Chae
2025-12-30 16:00 - 17:00
TBA
