Biomedical Mathematics Seminar

Abstract: This talk consists of two main parts. In the first part, I will discuss a numerical method for solving PDEs based on a stochastic representation of the solution. This …

Abstract: Weight calibration is a popular technique in handling covariate-shift problem in causal inference. It can be viewed as a dual optimization problem for incorporating the implicit regression model. We introduce the generalized entropy calibration as a general tool for weight calibration. Several interesting applications will be introduced in the context of causal inference. Furthermore, weight calibration can be used to transfer learning, which combines information from two different samples, one for source data and the other for target data.

Abstract  Topological Data Analysis (TDA) has emerged as a powerful framework for uncovering meaningful structure in high-dimensional, complex datasets. In this talk, we present two applications of TDA in analyzing patterns, one in …

Abstract Background : Excess mortality captures both the direct and indirect impacts of the pandemic. We examine (1) within-country heterogeneity by healthcare access over distinct viral waves in Korea, and …

본 발표는 삼성에서 개발 중인 디지털 헬스케어 기술을 소개합니다. 먼저, 삼성 갤럭시 웨어러블 센서의 기능과 활용 가능성을 설명하며, 웰니스 및 의료기기 서비스의 상품화 사례를 소개합니다. 또한, 삼성이 디지털 헬스케어를 통해 …

Digital health leverages information and communication technologies to transform healthcare, enabling diverse solutions for continuous health management. Among these, wearable-based digital health plays a key role by collecting, monitoring, and …

Abstract Recent emergence and re-emergence of infectious disease pathogens have caused major disruptions to our society, highlighting the importance of managing ongoing outbreaks and predicting future epidemics. In this talk, …

This seminar examines how generative AI advances three foundational tasks in causality, treated as distinct, modular problems: (1) causal inference via intervention‑effect estimation, (2) causal graph analysis, and (3) detection …

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 …

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 …

Mathematical modeling provides essential quantitative insights that accelerate drug and cell therapy development. In this presentation, we utilize kinetic frameworks to optimize the design of molecular glues by elucidating their …

Complex diseases, such as cancer, sarcopenia, and immune disorders, arise from abnormalities in multiple genes and pathways, posing significant challenges to conventional single-target drug discovery strategies. To address this, we …