• Dynamical Causality Under Latent Confounders for Biological Network Reconstruction – Olive Cawiding

    109, Conference room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    In this talk, we discuss the paper "Dynamical Causality Under Latent Confounders for Biological Network Reconstruction" by Jinling Yang et al, IEEE Transactions on Pattern Analysis and Machine Intelligence (2026). Abstract: Causal interaction inference is prone to spurious causal interactions, due to the substantial confounders in a biological system. While many existing methods attempt to address

  • 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