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  • A multi-agent reinforcement learning framework for exploring dominant strategies in iterated and evolutionary games – Fanpeng Song

    B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

    In this talk, we discuss the paper "A multi-agent reinforcement learning framework for exploring dominant strategies in iterated and evolutionary games" by Qi Su et al., Nat. Comm., 2026. Abstract Exploring dominant strategies in iterated games holds theoretical and practical significance across diverse domains. Previous studies, through mathematical analysis of limited cases, have unveiled classic …

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  • Stochastics in medicine: Delaying menopause and missing drug doses – Sean Lawley

    B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

    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 …

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  • A Data-Driven Computational Framework for Identifiability and Nonlinear Dynamics Discovery in Complex Systems – Wenrui Hao

    B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

    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. The first …

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  • Discovering network dynamics with neural symbolic regression – Olive Cawiding

    B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

    In this tallk, we discuss the paper "Discovering network dynamics with neural symbolic regression" by Zihan Yu et al., Nature Com. Science, 2026. Abstract Network 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 …

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  • Foundation Models for Wearable Movement Data in Mental Health Research – Aqsa Awan

    B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

    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. Abstract Pretrained 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 …

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  • Data-driven discovery of biological oscillator models – Lendert Gelens

    B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

    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 …

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  • Impact of daylight saving time on physical activity patterns – Myna Lim

    B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

    In this talk, we discuss the paper “Impact of daylight saving time on physical activity patterns” by Hayoung Jeong et al., Nature Health, 2026. Abstract Daylight saving time (DST) remains contentious: some policymakers highlight behavioural benefits, while others emphasize health risks. Here we estimated the behavioural and physiological impacts of DST using longitudinal Fitbit measures …

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  • High-order Michaelis-Menten equations allow inference of hidden kinetic parameters in enzyme catalysis – Hyeong Jun Jang

    B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

    In this talk, we discuss the paper "High-order Michaelis-Menten equations allow inference of hidden kinetic parameters in enzyme catalysis" by Divya Singh et al., Nat. Comm., 2025. Abstract Single-molecule measurements provide a platform for investigating the dynamical properties of enzymatic reactions. To this end, the single-molecule Michaelis-Menten equation was instrumental as it asserts that the …

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  • Heejung Shim – Modelling spatial transcriptomics: from flexible cell-type deconvolution to multi-scale spatial factor analysis

    B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

    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 …

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  • Mathematics of diffusive signaling – Alan Lindsay

    B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

    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 …

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  • Causal Generalist Medical AI – Hongtu Zhu

    ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

    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 …

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  • Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies – Chitaranjan Mahapatra

    B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

    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. Abstract: Accelerometers 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 …

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