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X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260403T110000
DTEND;TZID=Asia/Seoul:20260403T120000
DTSTAMP:20260505T180741
CREATED:20260205T073900Z
LAST-MODIFIED:20260311T121601Z
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SUMMARY:Stochastics in medicine: Delaying menopause and missing drug doses - Sean Lawley
DESCRIPTION:Stochastic modeling and analysis can help answer pressing medical questions. In this talk\, I will attempt to justify this claim by describing recent work on two problems in medicine. The first problem concerns ovarian tissue cryopreservation\, which is a proven tool to preserve ovarian follicles prior to gonadotoxic treatments. Can this procedure be applied to healthy women to delay or eliminate menopause? How can it be optimized? The second problem concerns medication nonadherence. What should you do if you miss a dose of medication? How can physicians design dosing regimens that are robust to missed/late doses? I will describe (a) how stochastics theory offers insights into these questions and (b) the mathematical questions that emerge from this investigation. \n  \nZoom : 997 8258 4700 (pw : 1234)
URL:https://www.ibs.re.kr/bimag/event/stochastics-in-medicine-delaying-menopause-and-missing-drug-doses-sean-lawley/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2026/02/Sean-Lawley-scaled-e1770278430433.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260410T110000
DTEND;TZID=Asia/Seoul:20260410T120000
DTSTAMP:20260505T180741
CREATED:20260205T074338Z
LAST-MODIFIED:20260311T121617Z
UID:12182-1775818800-1775822400@www.ibs.re.kr
SUMMARY:A Data-Driven Computational Framework for Identifiability and Nonlinear Dynamics Discovery in Complex Systems - Wenrui Hao
DESCRIPTION:Data-driven modeling is essential for deciphering complex biological systems\, yet its utility is often constrained by two fundamental hurdles: the inability to guarantee parameter identifiability and the high computational cost of learning nonlinear dynamics. This talk introduces a unified computational framework designed to overcome these challenges\, bridging theoretical rigor with scalable machine learning. \n\nThe first component of the framework establishes a computational foundation for practical identifiability. By leveraging the Fisher Information Matrix and its theoretical links to coordinate identifiability\, we propose an efficient method for identifiability assessment. We further introduce regularization-based strategies to manage non-identifiable parameters\, thereby enhancing model reliability and facilitating robust uncertainty quantification. \n\nTo address the discovery of nonlinear dynamics\, we present the Laplacian Eigenfunction-Based Neural Operator (LE-NO). This operator learning framework is specifically engineered for modeling reaction–diffusion equations. By projecting nonlinear operators onto Laplacian eigenfunctions\, LE-NO achieves superior computational efficiency and generalization across varying boundary conditions\, effectively bypassing the limitations of large-scale architectures and data scarcity. \n\nFinally\, we demonstrate the framework’s utility in the context of Alzheimer’s disease modeling. We show that this integrated approach ensures reliable parameter inference while capturing the intricate nonlinear dynamics of disease progression\, providing a critical step toward the development of high-fidelity digital twins for neurodegenerative pathology. \n  \nZoom : 997 8258 4700 (pw : 1234)
URL:https://www.ibs.re.kr/bimag/event/a-data-driven-computational-framework-for-identifiability-and-nonlinear-dynamics-discovery-in-complex-systems-wenrui-hao/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2026/02/Wenrui-Hao-2-e1770278378786.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260417T100000
DTEND;TZID=Asia/Seoul:20260417T120000
DTSTAMP:20260505T180741
CREATED:20260403T080037Z
LAST-MODIFIED:20260406T060603Z
UID:12336-1776420000-1776427200@www.ibs.re.kr
SUMMARY:Discovering network dynamics with neural symbolic regression - Olive Cawiding
DESCRIPTION:In this tallk\, we discuss the paper “Discovering network dynamics with neural symbolic regression” by Zihan Yu et al.\, Nature Com. Science\, 2026. \nAbstract  \nNetwork dynamics are fundamental to analyzing the properties of high-dimensional complex systems and understanding their behavior. Despite the accumulation of observational data across many domains\, mathematical models exist in only a few areas with clear underlying principles. Here we show that a neural symbolic regression approach can bridge this gap by automatically deriving formulas from data. Our method reduces searches on high-dimensional networks to equivalent one-dimensional systems and uses pretrained neural networks to guide accurate formula discovery. Applied to ten benchmark systems\, it recovers the correct forms and parameters of underlying dynamics. In two empirical natural systems\, it corrects existing models of gene regulation and microbial communities\, reducing prediction error by 59.98% and 55.94%\, respectively. In epidemic transmission across human mobility networks of various scales\, it discovers dynamics that exhibit the same power-law distribution of node correlations across scales and reveal country-level differences in intervention effects. These results demonstrate that machine-driven discovery of network dynamics can enhance understandings of complex systems and advance the development of complexity science.
URL:https://www.ibs.re.kr/bimag/event/discovering-network-dynamics-with-neural-symbolic-regression-olive-cawiding/
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:20260424T100000
DTEND;TZID=Asia/Seoul:20260424T120000
DTSTAMP:20260505T180741
CREATED:20260406T092550Z
LAST-MODIFIED:20260406T103749Z
UID:12365-1777024800-1777032000@www.ibs.re.kr
SUMMARY:Foundation Models for Wearable Movement Data in Mental Health Research - Aqsa Awan
DESCRIPTION:In this tallk\, we discuss the paper “Foundation Models for Wearable Movement Data in Mental Health Research” by Franklin Y. Ruan et al.\, arXiv\, 2025. \nAbstract \nPretrained foundation models and transformer architectures have driven the success of large language models (LLMs) and other modern AI breakthroughs. However\, similar advancements in health data modeling remain limited due to the need for innovative adaptations. Wearable movement data offers a valuable avenue for exploration\, as it’s a core feature in nearly all commercial smartwatches\, well established in clinical and mental health research\, and the sequential nature of the data shares similarities to language. We introduce the Pretrained Actigraphy Transformer (PAT)\, the first open source foundation model designed for time-series wearable movement data. Leveraging transformer-based architectures and novel techniques\, such as patch embeddings\, and pretraining on data from 29\,307 participants in a national U.S. sample\, PAT achieves state-of-the-art performance in several mental health prediction tasks. PAT is also lightweight and easily interpretable\, making it a robust tool for mental health research.
URL:https://www.ibs.re.kr/bimag/event/foundation-models-for-wearable-movement-data-in-mental-health-research-aqsa-awan/
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
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