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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:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251107T100000
DTEND;TZID=Asia/Seoul:20251107T120000
DTSTAMP:20260508T232322
CREATED:20251026T141100Z
LAST-MODIFIED:20251107T000601Z
UID:11791-1762509600-1762516800@www.ibs.re.kr
SUMMARY:Principled PCA separates signal from noise in omics count data - Hyun Kim
DESCRIPTION:In this talk\, we discuss the paper “Principled PCA separates signal from noise in omics count data” by Jay S. Stanley III et al.\, bioarxiv\, 2025.  \nAbstract \nPrincipal component analysis (PCA) is indispensable for processing high-throughput omics datasets\, as it can extract meaningful biological variability while minimizing the influence of noise. However\, the suitability of PCA is contingent on appropriate normalization and transformation of count data\, and accurate selection of the number of principal components; improper choices can result in the loss of biological information or corruption of the signal due to excessive noise. Typical approaches to these challenges rely on heuristics that lack theoretical foundations. In this work\, we present Biwhitened PCA (BiPCA)\, a theoretically grounded framework for rank estimation and data denoising across a wide range of omics modalities. BiPCA overcomes a fundamental difficulty with handling count noise in omics data by adaptively rescaling the rows and columns – a rigorous procedure that standardizes the noise variances across both dimensions. Through simulations and analysis of over 100 datasets spanning seven omics modalities\, we demonstrate that BiPCA reliably recovers the data rank and enhances the biological interpretability of count data. In particular\, BiPCA enhances marker gene expression\, preserves cell neighborhoods\, and mitigates batch effects. Our results establish BiPCA as a robust and versatile framework for high-throughput count data analysis.
URL:https://www.ibs.re.kr/bimag/event/from-noise-to-models-to-numbers-evaluating-negative-binomial-models-and-parameter-estimations-in-single-cell-rna-seq-hyun-kim/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251110T160000
DTEND;TZID=Asia/Seoul:20251110T163000
DTSTAMP:20260508T232322
CREATED:20251106T004905Z
LAST-MODIFIED:20251106T004905Z
UID:11838-1762790400-1762792200@www.ibs.re.kr
SUMMARY:Digital Health Care in Samsung - DongHyun Lee
DESCRIPTION:본 발표는 삼성에서 개발 중인 디지털 헬스케어 기술을 소개합니다. \n먼저\, 삼성 갤럭시 웨어러블 센서의 기능과 활용 가능성을 설명하며\, 웰니스 및 의료기기 서비스의 상품화 사례를 소개합니다. \n또한\, 삼성이 디지털 헬스케어를 통해 추구하는 방향과 비전을 제시합니다. \n마지막으로\, 삼성 개발자로서 디지털 헬스케어의 미래 전망과 기술 발전 가능성에 대해 논의합니다. \n이를 통해 디지털 헬스케어가 개인 건강 관리와 의료 산업에 미치는 영향을 조명합니다. \n  \n제목: Digital Health Care in Samsung \n목적: Samsung에서 개발하고 있는 Digital Health Care 소개 \n상세 내용: \n1. Samsung Galaxy Wearable Sensor 소개 \n2. Samsung 에서 상품화 하고 있는 Wellness 와 Medical Device 서비스 소개 \n3. Samsung이 Digital Health Care 를 바라보는 방향 \n4. Samsung 개발자로써 바라보는 Digital Health Care 의 미래
URL:https://www.ibs.re.kr/bimag/event/digital-health-care-in-samsung-donghyun-lee/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251110T163000
DTEND;TZID=Asia/Seoul:20251110T170000
DTSTAMP:20260508T232322
CREATED:20251106T005221Z
LAST-MODIFIED:20251106T005221Z
UID:11841-1762792200-1762794000@www.ibs.re.kr
SUMMARY:Bioinstrumentation System for Digital Health Platform: Sleep Health Monitoring Technologies Using Watch-Type Wearable - Hyunjun Jung
DESCRIPTION:Digital health leverages information and communication technologies to transform healthcare\, enabling diverse solutions for continuous health management. Among these\, wearable-based digital health plays a key role by collecting\, monitoring\, and analyzing physiological data over extended periods. In this lecture\, I will introduce the sleep-related features of Samsung’s Galaxy Watch series\, focusing on the biosignals that can be acquired from the wrist and how they are processed. I will also share practical insights from the research and validation processes that enabled these features. Through this\, I aim to show how your specialized\, in-depth research can be translated into real-world\, impactful digital health applications\, and what key factors must be considered in that process. \n  \nBiography \nHyunjun Jung is a Principle Engineer at Samsung Electronics’ MX Division (formerly Mobile Division)\, where he has been leading the research and development of various commercial solutions for the Galaxy Watch since April 2018. He played a pivotal role in the development of Samsung’s first wearable ECG device\, the Galaxy Watch Active 2\, and contributed to the FDA 510(k) approval for its AFib(Atrial Fibrillation) detection feature. Additionally\, he spearheaded the development of the world’s first FDA-approved (De Novo) sleep apnea detection function. He has also independently developed and validated solutions for oxygen saturation\, sleep score\, respiration rate during sleep\, and heart rate\, aiming to deliver more accurate health monitoring features to users.
URL:https://www.ibs.re.kr/bimag/event/bioinstrumentation-system-for-digital-health-platform-sleep-health-monitoring-technologies-using-watch-type-wearable-hyunjun-jung/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251112T150000
DTEND;TZID=Asia/Seoul:20251112T160000
DTSTAMP:20260508T232322
CREATED:20251019T091034Z
LAST-MODIFIED:20251019T091034Z
UID:11765-1762959600-1762963200@www.ibs.re.kr
SUMMARY:Mathematical modeling of infectious disease dynamics - Sang Woo Park
DESCRIPTION:Abstract \nRecent emergence and re-emergence of infectious disease pathogens have caused major disruptions to our society\, highlighting the importance of managing ongoing outbreaks and predicting future epidemics. In this talk\, I will use mathematical models to test biological hypotheses about pathogen transmission and leverage these findings to inform public health guidance. I will begin by modeling the transmission dynamics of Enterovirus D68 as a case study. I then use mathematical models from ecological perspective to answer questions about pathogen coexistence\, responses to perturbations\, and climate drivers. Overall\, I will provide a broad overview to highlight the use of mathematical models in answering core questions in infectious disease ecology.
URL:https://www.ibs.re.kr/bimag/event/mathematical-modeling-of-infectious-disease-dynamics-sang-woo-park/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251112T160000
DTEND;TZID=Asia/Seoul:20251112T170000
DTSTAMP:20260508T232322
CREATED:20250826T004501Z
LAST-MODIFIED:20251101T143038Z
UID:11471-1762963200-1762966800@www.ibs.re.kr
SUMMARY:(Cancelled) TBD - Amir Sharafkhaneh
DESCRIPTION:–
URL:https://www.ibs.re.kr/bimag/event/tbd-amir-sharafkhaneh/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/08/1516440570570-e1756169081265.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251112T160000
DTEND;TZID=Asia/Seoul:20251112T170000
DTSTAMP:20260508T232322
CREATED:20251107T041235Z
LAST-MODIFIED:20251107T042030Z
UID:11845-1762963200-1762966800@www.ibs.re.kr
SUMMARY:Generative Models and Causality - Kyungwoo Song
DESCRIPTION:This seminar examines how generative AI advances three foundational tasks in causality\, treated as distinct\, modular problems: (1) causal inference via intervention‑effect estimation\, (2) causal graph analysis\, and (3) detection of causal mechanism shifts and change points. First\, for causal inference\, we consider procedures in which generative models align domain knowledge with observational signals to represent treatment\, confounding\, and temporal context. This enables stable estimation of intervention effects and principled policy evaluation without relying on explicit counterfactual generation. Second\, for causal graph analysis\, we outline strategies that combine language‑grounded knowledge extraction and constraint proposals with statistical checks to improve the reliability of directionality and structure\, yielding interpretable hypothesis spaces and testable causal claims. Third\, for shift detection\, we describe methods that disentangle changes in functional mechanisms from changes in noise\, supporting early diagnosis of performance degradation\, targeting of monitoring resources\, and evidence‑based model updates in deployed settings. Across these tasks\, generative AI serves as a computational aide for knowledge alignment\, hypothesis proposal and pruning\, uncertainty annotation\, and experiment‑design suggestions. We conclude with a brief outlook on a causal agent that orchestrates data ingestion\, hypothesis formation\, intervention‑effect estimation\, shift monitoring\, and policy revision\, offering an integrated\, yet auditable and modular\, workflow for reliability‑centered decision support.
URL:https://www.ibs.re.kr/bimag/event/generative-models-and-causality-kyungwoo-song/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251121T100000
DTEND;TZID=Asia/Seoul:20251121T120000
DTSTAMP:20260508T232322
CREATED:20251026T141157Z
LAST-MODIFIED:20251121T000249Z
UID:11794-1763719200-1763726400@www.ibs.re.kr
SUMMARY:Modeling personalized heart rate response to exercise and environmental factors with wearables data - Dongju Lim
DESCRIPTION:In this talk\, we discuss the paper “Modeling personalized heart rate response to exercise and environmental factors with wearables data” by Nazaret et al.\, npj digital medicine\, 2023. \nAbstract \nHeart rate (HR) response to workout intensity re ects tness and cardiorespiratory health. Physiological models have been developed to describe such heart rate dynamics and characterize cardiorespiratory tness. However\, these models have been limited to small studies in controlled lab environments and are challenging to apply to noisy—but ubiquitous—data from wearables. We propose a hybrid approach that combines a physiological model with exible neural network components to learn a personalized\, multidimensional representation of tness. The physiological model describes the evolution of heart rate during exercise using ordinary differential equations (ODEs). ODE parameters are dynamically derived via a neural network connecting personalized representations to external environmental factors\, from area topography to weather and instantaneous workout\nintensity. Our approach ef ciently ts the hybrid model to a large set of 270\,707 workouts collected from wearables of 7465 users from the Apple Heart and Movement Study. The resulting model produces tness representations that accurately predict full HR response to exercise intensity in future workouts\, with a per-workout median error of 6.1 BPM [4.4–8.8 IQR]. We further demonstrate that the learned representations correlate with traditional metrics of cardiorespiratory tness\, such as VO2 max (explained variance\n0.81 ± 0.003). Lastly\, we illustrate how our model is naturally interpretable and explicitly describes the effects of environmental factors such as temperature and humidity on heart rate\, e.g.\, high temperatures can increase heart rate by 10%. Combining physiological ODEs with exible neural networks can yield interpretable\, robust\, and expressive models for health applications.
URL:https://www.ibs.re.kr/bimag/event/journal-club-dongju-lim/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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