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X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETTO:+0900
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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260821T100000
DTEND;TZID=Asia/Seoul:20260821T120000
DTSTAMP:20260730T035554Z
CREATED:20260730T020529Z
LAST-MODIFIED:20260730T035554Z
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SUMMARY:Tangent-Space Regularization for Neural-Network Models of Dynamical Systems - Olive Cawiding
DESCRIPTION: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). \nAbstract: \nThis 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 exhibits useful properties\, e.g.\, smoothness\, motivating regularization of the model Jacobian along system trajectories using assumptions on the tangent space of the dynamics. Without assumptions\, large amounts of training data are required for a neural network to learn the full non-linear dynamics without overfitting. We compare different network architectures on one-step prediction and simulation performance and investigate the propensity of different architectures to learn models with correct input-output Jacobian. Furthermore\, the influence of L2 weight regularization on the learned Jacobian eigenvalue spectrum\, and hence system stability\, is investigated.
URL:https://www.ibs.re.kr/bimag/event/tangent-space-regularization-for-neural-network-models-of-dynamical-systems-olive-cawiding/
LOCATION:108\, Conference Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260828T100000
DTEND;TZID=Asia/Seoul:20260828T120000
DTSTAMP:20260730T040743Z
CREATED:20260730T040743Z
LAST-MODIFIED:20260730T040743Z
UID:12776-1787911200-1787918400@www.ibs.re.kr
SUMMARY:Towards a General Intelligence and Interface for Wearable Health Data - Aqsa Awan
DESCRIPTION:In this talk\, we discuss the paper “Towards a General Intelligence and Interface for Wearable Health Data” by Girish Narayanswamy et al.\, arXiv\, 2026. \nAbstract: \nWhile 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 of characterizing higher-level states is difficult due to high phenotypic diversity and variation in individual baseline health\, physiology\, and lifestyle factors. Moreover\, collecting wearable data paired with health outcome annotations is laborious and expensive\, and retrospective annotation remains practically unfeasible\, contributing to a scarcity of data with high-quality labels. To overcome these limitations\, we propose a foundation model for wearable health that is pretrained on more than one trillion minutes of unlabeled sensor signals drawn from a large cohort of five million participants. We demonstrate that the joint scaling of model capacity and pretraining data volume leads to systematic improvements in performance\, as evaluated on a diverse set of 35 health prediction tasks\, spanning cardiovascular\, metabolic\, sleep\, and mental health\, as well as lifestyle choices and demographic factors. We find that this population scale representation unlocks label-efficient few-shot learning and generative capabilities for robust daily metric estimation. To further leverage this learned representation\, we deploy a classroom of LLM agents to autonomously search the space of downstream predictive heads built on the model embeddings\, showing broad performance improvements that increase with LLM model capacity. Finally\, we show how integrating these downstream predictors into a Personal Health Agent can support model responses that are more relevant\, contextually aware\, and safe\, and we validate this via 1\,860 ratings from a cohort of clinicians.
URL:https://www.ibs.re.kr/bimag/event/towards-a-general-intelligence-and-interface-for-wearable-health-data-aqsa-awan/
LOCATION:108\, Conference Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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