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X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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BEGIN:VTIMEZONE
TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260506T160000
DTEND;TZID=Asia/Seoul:20260506T170000
DTSTAMP:20260311T121631Z
CREATED:20260205T074722Z
LAST-MODIFIED:20260311T121631Z
UID:12189-1778083200-1778086800@www.ibs.re.kr
SUMMARY:Data-driven discovery of biological oscillator models - Lendert Gelens
DESCRIPTION:Oscillatory dynamics are a found everywhere in living systems\, underlying processes such as metabolic regulation\, cell division\, and embryonic development. Identifying the mechanisms that generate these rhythms is challenging due to nonlinear interactions\, multiple time scales\, and limited access to all relevant variables. Data-driven approaches offer a promising route to infer dynamical models directly from time-series data. In this talk\, I will discuss our work on data-driven discovery of models for (bio)chemical oscillators. In particular\, I will present CLINE\, a neural-network–based framework that infers key geometric features of phase space\, such as nullclines\, from oscillatory data and uses this information to construct low-dimensional dynamical models. \n  \nZoom : 997 8258 4700 (pw : 1234)
URL:https://www.ibs.re.kr/bimag/event/data-driven-discovery-of-biological-oscillator-models-lendert-gelens/
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/Gelens_Lendert_alumni-e1770278337776.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260522T110000
DTEND;TZID=Asia/Seoul:20260522T120000
DTSTAMP:20260311T121645Z
CREATED:20260205T075139Z
LAST-MODIFIED:20260311T121645Z
UID:12194-1779447600-1779451200@www.ibs.re.kr
SUMMARY:Mathematics of diffusive signaling - Alan Lindsay
DESCRIPTION:Diffusive transport is one of the most fundamental mechanisms by which information\, mass\, and chemical signals propagate in physical and biological systems. In many settings—ranging from cellular signaling to chemical sensing—communication is mediated by particles undergoing random motion and interacting with small\, spatially localized targets. This talk explores the mathematical structures underlying diffusive signaling\, emphasizing how geometry\, stochasticity\, and multiscale effects shape signal detection and reliability. Using tools from stochastic processes\, partial differential equations\, and asymptotic analysis\, I will describe how seemingly microscopic features can exert a dominant influence on macroscopic signaling outcomes\, and highlight recent progress on quantifying signal strength\, timing\, and variability in complex geometries. \n  \nZoom : 997 8258 4700 (pw : 1234)
URL:https://www.ibs.re.kr/bimag/event/mathematics-of-diffusive-signaling-alan-lindsay/
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/alan_lindsay-e1770278281837.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260527T160000
DTEND;TZID=Asia/Seoul:20260527T170000
DTSTAMP:20260523T023337Z
CREATED:20260523T013451Z
LAST-MODIFIED:20260523T023337Z
UID:12489-1779897600-1779901200@www.ibs.re.kr
SUMMARY:Causal Generalist Medical AI - Hongtu Zhu
DESCRIPTION:The rapid evolution of flexible and reusable artificial intelligence (AI) models is transforming medical science. This short course introduces Causal Generalist Medical AI (Causal GMAI)—a paradigm that integrates causal inference with generalist AI models to enhance interpretability\, robustness\, and generalizability in medical decision-making. Causal GMAI employs self-supervised\, semi-supervised\, and supervised learning on diverse multimodal datasets—including imaging\, electronic health records\, clinical trials\,  laboratory results\, genomics\, knowledge graphs\, and medical text—to perform a wide range of tasks with minimal task-specific supervision.  By embedding causal reasoning\, these models go beyond prediction to infer underlying causal relationships\, improving diagnostic accuracy\, treatment recommendations\, and personalized medicine. The course covers key technical components such as causal discovery\, counterfactual reasoning\, and domain adaptation\, alongside real-world applications.  We will also explore challenges in regulation\, validation\, and dataset curation to ensure clinical reliability and ethical deployment. Designed for researchers\, clinicians\, data scientists\, and AI practitioners\, this course provides a foundation for advancing the next generation of trustworthy and interpretable medical AI. \n  \nZoom : 997 8258 4700 (pw : 1234)
URL:https://www.ibs.re.kr/bimag/event/hongtu-zhu-tba/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2026/05/hongtu.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
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