• AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML – Jin Woo Hyun

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

    In this talk, we discuss the paper "AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML" by Patara Trirat et al, ICML, 2025. Abstract: Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline, such as optimal model search and hyperparameter tuning. Existing AutoML systems often require technical expertise to set up

  • Evolution of error correction through a need for speed – Kangmin Lee

    108, Conference Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    In this talk, we discuss the paper "Evolution of error correction through a need for speed" by Riccardo Ravasio et al., Science, 2026. Abstract: Kinetic proofreading is a class of error-correcting mechanisms in biology that expend energy to avoid mistakes during replication, transcription, and translation. Proofreading is typically assumed to evolve when selection for fidelity

  • Tangent-Space Regularization for Neural-Network Models of Dynamical Systems – Olive Cawiding

    108, Conference Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    In this talk, we discuss the paper "Tangent-Space Regularization for Neural-Network Models of Dynamical Systems" by Fredrik B Carlson et al, arXiv (2026). Abstract: This work introduces the concept of tangent space regularization for neural-network models of dynamical systems. The tangent space to the dynamics function of many physical systems of interest in control applications

  • Towards a General Intelligence and Interface for Wearable Health Data – Aqsa Awan

    108, Conference Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    In this talk, we discuss the paper "Towards a General Intelligence and Interface for Wearable Health Data" by Girish Narayanswamy et al., arXiv, 2026. Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging. Specifically, converting low-level sensor data into representations capable