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  • Identifying key drivers in a stochastic dynamical system through estimation of transfer entropy between univariate and multivariate time series – Yun Min Song

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

    In this talk, we discuss the paper "Identifying key drivers in a stochastic dynamical system through estimation of transfer entropy between univariate and multivariate time series" by Julian Lee, Physical Review E, 2025. Abstract  Transfer entropy (TE) is a widely used tool for quantifying causal relationships in stochastic dynamical systems. Traditionally, TE and its conditional …

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  • Dimensionality Reduction and Summary-Statistical Modeling in Genetic Studies – Fatemeh Yavartanoo

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

    Abstract: This presentation introduces DRLPC and a refined summary-statistics method to improve genetic association analysis. Applications to cognition, neurodegenerative diseases, and high cholesterol are discussed, with future directions in single-cell analysis and drug target discovery.

  • FoodSeq: Using Genomics to Track and Study Diet – Lawrence David

    Conference room, (B109) Daejeon, Daejeon, Korea, Republic of

    Abstract Dietary assessment is crucial for understanding the relationship between diet and health. Yet traditional recall-based methods for tracking diet often face challenges like participant compliance and accurate recall. To address these issues, our lab at Duke University has developed FoodSeq, a genomic approach to track food intake through DNA sequencing of stool samples. In …

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  • Boolean modelling as a logic-based dynamic approach in systems medicine – Kevin Spinicci

    In this talk, we discuss the paper "Boolean modelling as a logic-based dynamic approach in systems medicine" by Ahmed Abdelmonem Hemedan et al., Computational and Structural biotechnology journal (2022). Abstract  Molecular mechanisms of health and disease are often represented as systems biology diagrams, and the coverage of such representation constantly increases. These static diagrams can …

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  • Network inference from short, noisy, low time-resolution, partial measurements: Application to C. elegans neuronal calcium dynamics – Olive Cawiding

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

    In this talk, we discuss the paper "Network inference from short, noisy, low time-resolution, partial measurements: Application to C. elegans neuronal calcium dynamics" by Amitava Banerjee, Sarthak Chandra, and Edward Ott, PNAS, 2023. Abstract Network link inference from measured time series data of the behavior of dynamically interacting network nodes is an important problem with wide-ranging applications, e.g., estimating synaptic …

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  • Simplified descriptions of stochastic oscillators – Benjamin Lindner

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

    Abstract Many natural systems exhibit oscillations that show sizeable fluctuations in frequency and amplitude. This variability can arise from a wide variety of physical mechanisms. Phase descriptions that work for deterministic oscillators have a limited applicability for stochastic oscillators. In my talk I review attempts to generalize the phase concept to stochastic oscillations, specifically, the …

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  • Koopman operator approach to complex rhythmic systems – Hiroya Nakao

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

    Abstract Spontaneous rhythmic oscillations are widely observed in real-world systems. Synchronized rhythmic oscillations often provide important functions for biological or engineered systems. One of the useful theoretical methods for analyzing rhythmic oscillations is the phase reduction theory for weakly perturbed limit-cycle oscillators, which systematically gives a low-dimensional description of the oscillatory dynamics using only the …

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  • Direct Estimation of Parameters in ODE Models Using WENDy – Kangmin Lee

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

    In this talk, we discuss the paper "Direct Estimation of Parameters in ODE Models Using WENDy: Weak-Form Estimation of Nonlinear Dynamics" by David M. Bortz, Daniel A. Messenger, and Vanja Dukic, Bulletin of Mathematical Biology, 2023. Abstract We introduce the Weak-form Estimation of Nonlinear Dynamics (WENDy) method for estimating model parameters for non-linear systems of …

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  • Deep learning for universal linear embeddings of nonlinear dynamics – Hyukpyo Hong

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

    In this talk, we discuss the paper "Deep learning for universal linear embeddings of nonlinear dynamics" by B. Lusch, J. N. Kutz, and S. Brunton, Nat. Comm. 2018. Abstract  Identifying coordinate transformations that make strongly nonlinear dynamics approximately linear has the potential to enable nonlinear prediction, estimation, and control using linear theory. The Koopman operator …

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  • Large language models for scientific discovery in molecular property prediction – Aqsa Awan

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

    In this talk, we discuss the paper "Large language models for scientific discovery in molecular property prediction" by Yizhen Zheng et.al., nature machine intelligence, 2025. Abstract Large language models (LLMs) are a form of artificial intelligence system encapsulating vast knowledge in the form of natural language. These systems are adept at numerous complex tasks including …

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  • Data splitting to avoid information leakage with DataSAIL – Myna Lim

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

    In this talk, we discuss the paper, "Data splitting to avoid information leakage with DataSAIL" by Roman Joeres, et al., Nature Communications, 2025. Abstract Information leakage is an increasingly important topic in machine learning research for biomedical applications. When information leakage happens during a model’s training, it risks memorizing the training data instead of learning …

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  • U Jin Choi – Simulation-Free Schrodinger Bridges Via Score and Flow Matching (by Tong et al, AISTATS 2024).

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

    Abstract: 임의로 정한 Initial Distribution Q1 와 Terminal Distribution Q2가 주어 졌을 때 시점과 종점 사이의 contiinious time상에  정의 되는 의미 있는 최적의 Probability Path Measure P 를 찾는 Schrodinger Bridges Problem 은 자연과학,공학, 의료 및 생명공학,경제학 및 금융공학 등의 여러 분야에 나타나는 모델들을 푸는 Unified AI Model 사용 되고 있습니다. Schrodinger Bridges Problem은  유일한 해가 존재 하는 정리는( Follmer,1988)  증명 되었으므로 데이터를 이용하여  Neural Network Models에 대한 효율적으로 학습방법,  빠른 알고리즘 연구에 집중 되고 있습니다. Tong et al 연구팀은 2023년 부터 ODE에 기반한 획기적인 생성모델인  Flow Matching for Generative Modeling 기법을  SDE 기반 Diffusion Generative Models에 접목하여 Schrodinger Bridges Problem의 해법을 제시하였습니다.