• Mathematical modeling of infectious disease dynamics – Sang Woo Park

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

    Abstract Recent emergence and re-emergence of infectious disease pathogens have caused major disruptions to our society, highlighting the importance of managing ongoing outbreaks and predicting future epidemics. In this talk, I will use mathematical models to test biological hypotheses about pathogen transmission and leverage these findings to inform public health guidance. I will begin by

  • Generative Models and Causality – Kyungwoo Song

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

    This seminar examines how generative AI advances three foundational tasks in causality, treated as distinct, modular problems: (1) causal inference via intervention‑effect estimation, (2) causal graph analysis, and (3) detection of causal mechanism shifts and change points. First, for causal inference, we consider procedures in which generative models align domain knowledge with observational signals to

  • Homeostatic forces underlying the daily pattern of sleep propensity – Dongju Lim

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

    In this talk, we discuss the paper "Homeostatic forces underlying the daily pattern of sleep propensity" by Vasili Kharchenko et al., SLEEP Advances, 2025. Abstract The mismatch between rising sleep need and the fluctuating ability to fall asleep underlies insomnia—the most common sleep disorder—yet remains poorly understood. While sleep need increases steadily with time awake,

  • Quantifying interventional causality by knockoff operation – Yun Min Song

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

    In this talk, we discuss the paper "Quantifying interventional causality by knockoff operation" by Xinyan Zhang and Luonan Chen, Science Advances, 2025. Abstract  Causal inference between measured variables is crucial to understand the underlying mechanism of complex biological processes at a network level but remains challenging in computational biology. We propose an innovative causal criterion,

  • FilterNet: Harnessing Frequency Filters for Time Series Forecasting – Jinwoo Hyun

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

    In this talk, we discuss the paper "FilterNet: Harnessing Frequency Filters for Time Series Forecasting" by Kun Yi et al., NeurIPS, 2024. Abstract Given the ubiquitous presence of time series data across various domains, precise forecasting of time series holds significant importance and finds widespread real-world applications such as energy, weather, healthcare, etc. While numerous

  • Empirical modeling of bifurcations and chaos from time series – Stephan Munch

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

    Abstract Many 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

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IBS Biomedical Mathematics Group (BIMAG)
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