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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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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260814T100000
DTEND;TZID=Asia/Seoul:20260814T120000
DTSTAMP:20260730T035435Z
CREATED:20260730T035435Z
LAST-MODIFIED:20260730T035435Z
UID:12774-1786701600-1786708800@www.ibs.re.kr
SUMMARY:Evolution of error correction through a need for speed - Kangmin Lee
DESCRIPTION:In this talk\, we discuss the paper “Evolution of error correction through a need for speed” by Riccardo Ravasio et al.\, Science\, 2026. \nAbstract: \nKinetic 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 outweighs costs in energy and the speed of replication. We show that when stalling after misincorporations is accounted for\, proofreading can instead speed up replication. Consistent with data on polymerase mutagenesis\, our results suggest that proofreading can evolve under selection for speed alone. We generalize to multicomponent self-assembly and show that analogous error-correcting processes\, such as dynamic instability\, can likewise emerge purely from selection for rapid assembly. Thus\, nonequilibrium error correction can evolve from selection for speed\, even without direct fidelity advantages. We discuss implications for mutation-rate evolution\, molecular assembly processes\, and models of early life.
URL:https://www.ibs.re.kr/bimag/event/evolution-of-error-correction-through-a-need-for-speed-kangmin-lee/
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260817T160000
DTEND;TZID=Asia/Seoul:20260817T170000
DTSTAMP:20260810T020452Z
CREATED:20260810T020342Z
LAST-MODIFIED:20260810T020452Z
UID:12808-1786982400-1786986000@www.ibs.re.kr
SUMMARY:수학이 공학의 언어라면 - 민태기
DESCRIPTION:  \n이번 세미나에서는 “조선이 만난 아인슈타인”\, “판타 레이” 등을 저술하신 민태기 소장님을 모시고 “수학이 공학의 언어라면”이라는 주제로 강연을 진행할 예정입니다.
URL:https://www.ibs.re.kr/bimag/event/%ec%88%98%ed%95%99%ec%9d%b4-%ea%b3%b5%ed%95%99%ec%9d%98-%ec%96%b8%ec%96%b4%eb%9d%bc%eb%a9%b4-%eb%af%bc%ed%83%9c%ea%b8%b0/
LOCATION:109\, Conference room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2026/08/L20230605095029.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260821T100000
DTEND;TZID=Asia/Seoul:20260821T120000
DTSTAMP:20260818T042157Z
CREATED:20260730T020529Z
LAST-MODIFIED:20260818T042157Z
UID:12772-1787306400-1787313600@www.ibs.re.kr
SUMMARY:Dynamical Causality Under Latent Confounders for Biological Network Reconstruction - Olive Cawiding
DESCRIPTION: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). \nAbstract: \nCausal interaction inference is prone to spurious causal interactions\, due to the substantial confounders in a biological system. While many existing methods attempt to address misidentification challenges\, there remains a notable lack of effective methods to infer causal interaction under latent/unobserved confounders. In this work\, we propose a method to overcome such challenges to infer dynamical causality under invisible confounders (CIC) and further reconstruct the latent confounders from time-series data by developing an orthogonal decomposition theorem in a delay embedding space. This theoretical foundation ensures the causal detection for any high-dimensional system even with only two observed variables under many latent confounders\, which is a long-standing problem in the field. In addition to the latent confounder problem\, such a decomposition makes the coupled variables separable in the embedding space\, thus also solving the non-separability problem of causal inference. Extensive validation of the CIC method is carried out using various real datasets\, which all demonstrates its effectiveness to reconstruct real biological networks and unobserved confounders.
URL:https://www.ibs.re.kr/bimag/event/tangent-space-regularization-for-neural-network-models-of-dynamical-systems-olive-cawiding/
LOCATION:109\, 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
END:VEVENT
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
END:VEVENT
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