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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:20251112T150000
DTEND;TZID=Asia/Seoul:20251112T160000
DTSTAMP:20260430T165813
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251112T160000
DTEND;TZID=Asia/Seoul:20251112T170000
DTSTAMP:20260430T165813
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
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END:VCALENDAR