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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:20260522T110000
DTEND;TZID=Asia/Seoul:20260522T120000
DTSTAMP:20260527T180037
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:20260527T180037
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
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260529T100000
DTEND;TZID=Asia/Seoul:20260529T120000
DTSTAMP:20260527T180037
CREATED:20260429T070610Z
LAST-MODIFIED:20260518T051101Z
UID:12398-1780048800-1780056000@www.ibs.re.kr
SUMMARY:Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies - Chitaranjan Mahapatra
DESCRIPTION:In this talk\, we discuss the paper “Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies” by Sam vidil et al.\, Nature Communications\, 2025. \nAbstract: \nAccelerometers allow objective measures of dimensions (rest-activity rhythm (RAR)\, daytime activity\, sleep\, and chronotype) of the bio-behavioural manifestation of circadian rhythm (CR) using multiple metrics in large-scale studies. These dimensions are rarely examined together due to methodological challenges of using correlated data. To address this challenge\, we propose a two-step approach consisting of data reduction of CR metrics using principal component analyses\, followed by k-means clustering to identify groups of individuals with a similar profile using data from the Whitehall II (N = 3\,991\, mean age=69.4years) and UK Biobank (N = 54\,995\, mean age=67.5years) cohort studies. Our analyses identified nine CR clusters: two presented extreme (most robust/poorest) RAR and (highest/lowest) daytime activity\, two robust RAR with opposite sleep profiles (longer and efficient/shorter and fragmented)\, one high-intensity physical activity\, and four poor RAR (one characterised by late chronotype\, two by low activity but opposite sleep profiles\, and one by restless (agitated) sleep). The participants in these nine clusters differed on sociodemographic\, behavioural and health-related factors. Findings were similar in these two independent cohort studies\, highlighting the validity of our approach. Most previous studies have used only the RAR dimension of circadian rhythm\, and here we show that this might be an oversimplification as demonstrated by nine clusters characterised by combinations of RAR\, daytime activity\, sleep\, and chronotype. Our innovative approach demonstrates feasibility of using all dimensions to study the impact of circadian rhythm dysregulation on health.
URL:https://www.ibs.re.kr/bimag/event/circadian-rhythm-profiles-derived-from-accelerometer-measures-of-the-sleep-wake-cycle-in-two-cohort-studies-chitaranjan-mahapatra/
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:20260605T100000
DTEND;TZID=Asia/Seoul:20260605T120000
DTSTAMP:20260527T180037
CREATED:20260429T070216Z
LAST-MODIFIED:20260527T003414Z
UID:12396-1780653600-1780660800@www.ibs.re.kr
SUMMARY:Chaos Meets Attention: Transformers for Large-Scale Dynamical Prediction- Gyuyoung Hwang
DESCRIPTION:In this talk\, we discuss the paper “Chaos Meets Attention: Transformers for Large-Scale Dynamical Prediction” by Yi He et al.\, ICML Poster\, 2025. \nAbstract: \nGenerating long-term trajectories of dissipative chaotic systems autoregressively is a highly challenging task. The inherent positive Lyapunov exponents amplify prediction errors over time. Many chaotic systems possess a crucial property — ergodicity on their attractors\, which makes long-term prediction possible. State-of-the-art methods address ergodicity by preserving statistical properties using optimal transport techniques. However\, these methods face scalability challenges due to the curse of dimensionality when matching distributions. To overcome this bottleneck\, we propose a scalable transformer-based framework capable of stably generating long-term high-dimensional and high-resolution chaotic dynamics while preserving ergodicity. Our method is grounded in a physical perspective\, revisiting the Von Neumann mean ergodic theorem to ensure the preservation of long-term statistics in the L2 space. We introduce novel modifications to the attention mechanism\, making the transformer architecture well-suited for learning large-scale chaotic systems. Compared to operator-based and transformer-based methods\, our model achieves better performances across five metrics\, from short-term prediction accuracy to long-term statistics. In addition to our methodological contributions\, we introduce new chaotic system benchmarks: a machine learning dataset of 140 snapshots of turbulent channel flow and a processed high-dimensional Kolmogorov Flow dataset\, along with various evaluation metrics for both short- and long-term performances. Both are well-suited for machine learning research on chaotic systems.
URL:https://www.ibs.re.kr/bimag/event/bridging-known-and-unknown-dynamics-by-transformer-based-machine-learning-inference-from-sparse-observations-gyuyoung-hwang/
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:20260612T100000
DTEND;TZID=Asia/Seoul:20260612T120000
DTSTAMP:20260527T180037
CREATED:20260520T075146Z
LAST-MODIFIED:20260520T075146Z
UID:12428-1781258400-1781265600@www.ibs.re.kr
SUMMARY:Insulin resistance prediction from wearables and routine blood biomarkers - Dongju Lim
DESCRIPTION:In this talk\, we discuss the paper “Insulin resistance prediction from wearables and routine blood biomarkers” by Ahmed A. Metwally et al.\, Nature\, 2026. \n  \nAbstract: \nInsulin resistance (IR)\, a primary precursor to type 2 diabetes\, is characterized by impaired insulin action in tissues1. However\, diagnostic methods remain expensive and inaccessible\, which hinders early intervention2\,3. Here we present the WEAR-ME study\, a large\, remotely conducted study of IR (n = 1\,165 participants; median body mass index (BMI) = 28 kg m−2\, median age = 45 years\, median haemoglobin A1c (HbA1c) = 5.4%) that uses time-series data from wearable devices and routine blood biomarkers to train deep neural networks against a ground-truth measure of IR (homeostatic model assessment of IR; HOMA-IR). Using a HOMA-IR cut-off of 2.9\, our multimodal model achieved robust performance (area under the receiver operating characteristic curve (AUROC) = 0.80\, sensitivity = 76%\, specificity = 84%) with data from wearable devices\, together with demographic and routine blood biomarker data. To enhance the use of time-series data from wearables\, we fine-tuned a wearable foundation model (WFM) pretrained on 40 million hours of sensor data. In an independent validation cohort (n = 72)\, a model integrating WFM-derived representations with demographic data surpassed a demographics-only baseline (AUROC = 0.75 versus 0.66). Moreover\, adding WFM-derived representations to a model with demographics\, fasting glucose and a lipid panel substantially improved performance\, compared with an identical model without data from wearables (AUROC = 0.88 versus 0.76). We integrate IR prediction into a large language model to contextualize the results and facilitate personalized recommendations. This work establishes a scalable\, accessible framework for the early detection of metabolic risk\, which could enable timely lifestyle interventions to prevent progression to type 2 diabetes.
URL:https://www.ibs.re.kr/bimag/event/insulin-resistance-prediction-from-wearables-and-routine-blood-biomarkers-dongju-lim/
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
END:VCALENDAR