BEGIN:VCALENDAR
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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20240101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251201T093000
DTEND;TZID=Asia/Seoul:20251202T150000
DTSTAMP:20260508T220003
CREATED:20251125T084153Z
LAST-MODIFIED:20251125T084415Z
UID:11903-1764581400-1764687600@www.ibs.re.kr
SUMMARY:2025 KAI-X Global Conference in Sleep Synergy
DESCRIPTION:Conference Webpage Link: https://sites.google.com/view/2025-kai-x-sleep-synergy/home
URL:https://www.ibs.re.kr/bimag/event/2025-kai-x-global-conference-in-sleep-synergy/
LOCATION:KAIST W13 Conference Room (1F)\, 291 Daehak-ro Yuseong-gu\, Daejeon\, Daejeon\, 34141\, Korea\, Republic of
CATEGORIES:Workshops and Conferences
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/11/unnamed-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251205T110000
DTEND;TZID=Asia/Seoul:20251205T120000
DTSTAMP:20260508T220003
CREATED:20250826T005255Z
LAST-MODIFIED:20250826T005331Z
UID:11476-1764932400-1764936000@www.ibs.re.kr
SUMMARY:Empirical modeling of bifurcations and chaos from time series - Stephan Munch
DESCRIPTION:Abstract \nMany natural systems exhibit complex dynamics and are prone to sudden changes or ‘regime shifts’. At the same time\, many of these systems are sparsely observed posing considerable challenges for modeling and control. Here I will describe recent developments in empirical dynamic modeling (EDM) for inference of bifurcations and anticipation of unseen dynamical regimes from ecological time series.
URL:https://www.ibs.re.kr/bimag/event/empirical-modeling-of-bifurcations-and-chaos-from-time-series-stephan-munch/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/08/head2-e1756169564670.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251212T110000
DTEND;TZID=Asia/Seoul:20251212T123000
DTSTAMP:20260508T220003
CREATED:20251026T141250Z
LAST-MODIFIED:20251215T043112Z
UID:11796-1765537200-1765542600@www.ibs.re.kr
SUMMARY:Quantifying interventional causality by knockoff operation - Yun Min Song
DESCRIPTION: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. \nAbstract  \nConducting 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.
URL:https://www.ibs.re.kr/bimag/event/journal-club-yun-min-song/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251226T100000
DTEND;TZID=Asia/Seoul:20251226T120000
DTSTAMP:20260508T220003
CREATED:20251026T141349Z
LAST-MODIFIED:20251226T002150Z
UID:11798-1766743200-1766750400@www.ibs.re.kr
SUMMARY:N-BEATS: Neural basis expansion analysis for interpretable time series forecasting - Jinwoo Hyun
DESCRIPTION:In this talk\, we discuss the paper “N-BEATS: Neural basis expansion analysis for interpretable time series forecasting” by B. Oreshkin et al.\, ICLR\, 2020. \nAbstract \nWe focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties\, being interpretable\, applicable without modification to a wide array of target domains\, and fast to train. We test the proposed architecture on several well-known datasets\, including M3\, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for two configurations of N-BEATS for all the datasets\, improving forecast accuracy by 11% over a statistical benchmark and by 3% over last year’s winner of the M4 competition\, a domain-adjusted hand-crafted hybrid between neural network and statistical time series models. The first configuration of our model does not employ any time-series-specific components and its performance on heterogeneous datasets strongly suggests that\, contrarily to received wisdom\, deep learning primitives such as residual blocks are by themselves sufficient to solve a wide range of forecasting problems. Finally\, we demonstrate how the proposed architecture can be augmented to provide outputs that are interpretable without considerable loss in accuracy.
URL:https://www.ibs.re.kr/bimag/event/journal-club-jinwoo-hyun/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251229T163000
DTEND;TZID=Asia/Seoul:20251229T183000
DTSTAMP:20260508T220003
CREATED:20251224T001444Z
LAST-MODIFIED:20251224T001528Z
UID:12049-1767025800-1767033000@www.ibs.re.kr
SUMMARY:Distribution shift in machine learning: robustness\, invariance\, and a causal view - Wooseok Ha
DESCRIPTION: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.
URL:https://www.ibs.re.kr/bimag/event/distribution-shift-in-machine-learning-robustness-invariance-and-a-causal-view-wooseok-ha/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251230T150000
DTEND;TZID=Asia/Seoul:20251230T160000
DTSTAMP:20260508T220003
CREATED:20251224T001705Z
LAST-MODIFIED:20251224T001718Z
UID:12052-1767106800-1767110400@www.ibs.re.kr
SUMMARY:Expanding the Data Analysis Toolkit: Explainable AI\, Causal Learning\, and Time-Series Foundation Models - Daeil Jang
DESCRIPTION: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. \nFirst\, 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. \nRather 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.
URL:https://www.ibs.re.kr/bimag/event/expanding-the-data-analysis-toolkit-explainable-ai-causal-learning-and-time-series-foundation-models-daeil-jang/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251230T160000
DTEND;TZID=Asia/Seoul:20251230T170000
DTSTAMP:20260508T220003
CREATED:20251224T001815Z
LAST-MODIFIED:20251229T112942Z
UID:12054-1767110400-1767114000@www.ibs.re.kr
SUMMARY:Rationalizing Therapeutics: Mathematical Insights into Drug and Cell Therapy Development - Seokjoo Chae
DESCRIPTION: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 advance the development of next-generation therapeutics.
URL:https://www.ibs.re.kr/bimag/event/rationalizing-therapeutics-mathematical-insights-into-drug-and-cell-therapy-development-seokjoo-chae/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR