A biological model of nonlinear dimensionality reduction – Shingo Gibo

In this talk, we discuss the paper "A biological model of nonlinear dimensionality reduction" by K. Yoshida and T. Toyoizumi, Science Advances, 2025, at the Journal Club. Abstract Obtaining appropriate low-dimensional representations from high-dimensional sensory inputs in an unsupervised manner is essential for straightforward downstream processing. Although nonlinear dimensionality reduction methods such as t-distributed stochastic neighbor

Designing microplastic-binding peptides with a variational quantum circuit–based hybrid quantum-classical approach – Gyuyoung Hwang

In this talk, we discuss the paper "Designing microplastic-binding peptides with a variational quantum circuit–based hybrid quantum-classical approach" by R.C. Vendrell et.al., Sci. Adv. 2024 at the Journal Club. Abstract De novo peptide design exhibits great potential in materials engineering, particularly for the use of plastic-binding peptides to help remediate microplastic pollution. There are no

Frequency-Dependent Covariance Reveals Critical Spatiotemporal Patterns of Synchronized Activity in the Human Brain – Hyun Kim

In this talk, we discuss the paper "Frequency-Dependent Covariance Reveals Critical Spatiotemporal Patterns of Synchronized Activity in the Human Brain" by Rubén Calvo et al., Physical Review Letters 2024, at the Journal Club. Abstract Recent analyses, leveraging advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons across regions in the brain, compellingly

Accurate predictions on small data with a tabular foundation model – Dongju Lim

In this talk, we discuss the paper "Accurate predictions on small data with a tabular foundation model" by Noah Hollmann et al., Nature (2025). Abstract Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science1,2. The fundamental prediction task of filling in

Entrainment and multi-stability of the p53 oscillator in human cells – Eui Min Jeong

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

In this talk, we discuss the paper, "Entrainment and multi-stability of the p53 oscillator in human cells" by Alba Jiménez et al., Cell Systems, 2024. Abstract  The tumor suppressor p53 responds to cellular stress and activates transcription programs critical for regulating cell fate. DNA damage triggers oscillations in p53 levels with a robust period. Guided by

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

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

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

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

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

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

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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