Biomedical Mathematics Group

Quantifying interventional causality by knockoff operation - Yun Min Song
2025-12-12 11:00 - 12:30 (Asia/Seoul) | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Causal disentanglement for single-cell representations and controllable counterfactual generation” by Yicheng Gao et al., Nature Communications, 2025.
Abstract
Conducting disentanglement learning on single-cell omics data offers a promising alternative to traditional black-box representation learning by separating the semantic concepts embedded in a biological process. We present CausCell, which incorporates the factual information about causal relationships among disentangled concepts within a diffusion model to generate more reliable disentangled cellular representations, with the aim of increasing the explainability, generalizability and controllability of single-cell data, including spatial-temporal omics data, relative to those of the existing black-box representation learning models. Two quantitative evaluation scenarios, i.e., disentanglement and reconstruction, are presented to conduct the first comprehensive single-cell disentanglement learning benchmark, which demonstrates that CausCell outperforms the state-of-the-art methods in both scenarios. Additionally, CausCell can implement controllable generation by intervening with the concepts of single-cell data when given a causal structure. It also has the potential to uncover biological insights by generating counterfactuals from small and noisy single-cell datasets.

FilterNet: Harnessing Frequency Filters for Time Series Forecasting - Jinwoo Hyun
2025-12-19 10:00 - 12:00
In this talk, we discuss the paper “FilterNet: Harnessing Frequency Filters for Time Series Forecasting” by Kun Yi et al., NeurIPS, 2024.
Abstract
Given the ubiquitous presence of time series data across various domains, precise forecasting of time series holds significant importance and finds widespread real-world applications such as energy, weather, healthcare, etc. While numerous forecasters have been proposed using different network architectures, the Transformer-based models have state-of-the-art performance in time series forecasting. However, forecasters based on Transformers are still suffering from vulnerability to high-frequency signals, efficiency in computation, and bottleneck in full-spectrum utilization, which essentially are the cornerstones for accurately predicting time series with thousands of points. In this paper, we explore a novel perspective of enlightening signal processing for deep time series forecasting. Inspired by the filtering process, we introduce one simple yet effective network, namely FilterNet, built upon our proposed learnable frequency filters to extract key informative temporal patterns by selectively passing or attenuating certain components of time series signals. Concretely, we propose two kinds of learnable filters in the FilterNet: (i) Plain shaping filter, that adopts a universal frequency kernel for signal filtering and temporal modeling; (ii) Contextual shaping filter, that utilizes filtered frequencies examined in terms of its compatibility with input signals for dependency learning. Equipped with the two filters, FilterNet can approximately surrogate the linear and attention mappings widely adopted in time series literature, while enjoying superb abilities in handling high-frequency noises and utilizing the whole frequency spectrum that is beneficial for forecasting. Finally, we conduct extensive experiments on eight time series forecasting benchmarks, and experimental results have demonstrated our superior performance in terms of both effectiveness and efficiency compared with state-of-the-art methods. Our code is available at [1].

Distribution shift in machine learning: robustness, invariance, and a causal view - Wooseok Ha
2025-12-29 16:30 - 18:30
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
