Biomedical Mathematics Seminar

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 analyzing physiological data over extended periods. In this lecture, I will introduce the sleep-related features of Samsung’s Galaxy Watch series, focusing on the biosignals that …

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, I will use mathematical models to test biological hypotheses about pathogen transmission and leverage these findings to inform public health guidance. I will begin by …

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 of causal mechanism shifts and change points. First, for causal inference, we consider procedures in which generative models align domain knowledge with observational signals to …

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 …

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 …

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 biophysical determinants and identify a key target for NK cell-mediated immunotherapy through systematic data analysis. Collectively, we demonstrate how mathematical strategies can effectively guide and …

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 developed a perturbome-based analytical framework that integrates transcriptomic signatures, network pharmacology, and machine learning to identify effective therapeutic candidates. Central to this approach is the …

Abstract (국문) 최근 거대언어모델(LLM)을 기술의 발전은 AI4Science 분야에서 Foundation Model 개발에 대한 세계적인 관심을 촉발하였다. 그 중에서도 신약 및 신소재 개발에 연계된 Molecular 도메인에서의 Foundation Model 연구는 막대한 산업적 영향력과 가치를 가지고 있다. 본 발표에서는 분자 구조 생성, 물성, 및 반응 예측 문제에 적용되기 위해 필요한 Multimodal LLM 연구 성과와 방향성을 소개하고자 한다. (English) The …

Abstract: Spatial transcriptomics enables the study of gene expression within its spatial context, but introduces key statistical challenges, including mixed cellular composition and complex spatial structure. In this talk, I present two complementary modelling approaches.First, I introduce FlexiDeconv, a cell-type deconvolution method based on a modified Latent Dirichlet Allocation framework. A key feature of this …

Abstract: Accurately predicting mood fluctuations in mood disorders is critical for early intervention and personalized treatment. This study developed a neurophysiologically grounded mood prediction model by integrating behavioral modeling, electroencephalography, functional magnetic resonance imaging (fMRI), and physiological data from wearable devices in premenstrual syndrome (PMS). First, applying the active inference framework to a risk-taking behavioral …

Abstract: Evolutionary biology studies populations of reproducing individuals and how their composition changes over time.An important question is the fixation probability of a single mutant that attempts to invade a homogeneous population.Many real populations experience gradients of chemicals or nutrients that cause mutations to be beneficial in some spatial regions and harmful in others.We will …

Abstract: A central goal of scientific computing is to develop accurate and efficient solvers for scientific problems, and this goal is often pursued through sophisticated numerical methods. In modern machine learning, by contrast, the basic optimization procedure is often comparatively simple, typically gradient descent and its variants, while much of the complexity is shifted to …