• 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

  • 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

  • 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

  • Digital biomarkers for brain health: passive and continuous assessment from wearable sensors – Myna Lim

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

    In this tallk, we discuss the paper “Digital biomarkers for brain health: passive and continuous assessment from wearable sensors” by Igor Matias et al., npj digital medicine, 2026. Abstract Continuous and scalable monitoring of cognition and affective states is critical for the early detection of brain health, which is currently limited by the burden of

  • 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 tallk, 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

  • 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