BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20240101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250613T093000
DTEND;TZID=Asia/Seoul:20250613T110000
DTSTAMP:20260423T030304
CREATED:20250609T002038Z
LAST-MODIFIED:20250609T033628Z
UID:11164-1749807000-1749812400@www.ibs.re.kr
SUMMARY:Deep learning for universal linear embeddings of nonlinear dynamics - Hyukpyo Hong
DESCRIPTION:In this talk\, we discuss the paper “Deep learning for universal linear embeddings of nonlinear dynamics” by B. Lusch\, J. N. Kutz\, and S. Brunton\, Nat. Comm. 2018. \nAbstract  \nIdentifying coordinate transformations that make strongly nonlinear dynamics approximately linear has the potential to enable nonlinear prediction\, estimation\, and control using linear theory. The Koopman operator is a leading data-driven embedding\, and its eigenfunctions provide intrinsic coordinates that globally linearize the dynamics. However\, identifying and representing these eigenfunctions has proven challenging. This work leverages deep learning to discover representations of Koopman eigenfunctions from data. Our network is parsimonious and interpretable by construction\, embedding the dynamics on a low-dimensional manifold. We identify nonlinear coordinates on which the dynamics are globally linear using a modified auto-encoder. We also generalize Koopman representations to include a ubiquitous class of systems with continuous spectra. Our framework parametrizes the continuous frequency using an auxiliary network\, enabling a compact and efficient embedding\, while connecting our models to decades of asymptotics. Thus\, we benefit from the power of deep learning\, while retaining the physical interpretability of Koopman embeddings.
URL:https://www.ibs.re.kr/bimag/event/hyukpyo-hong/
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:20250620T110000
DTEND;TZID=Asia/Seoul:20250620T123000
DTSTAMP:20260423T030304
CREATED:20250426T143500Z
LAST-MODIFIED:20250617T001232Z
UID:11064-1750417200-1750422600@www.ibs.re.kr
SUMMARY:Large language models for scientific discovery in molecular property prediction - Aqsa Awan
DESCRIPTION:In this talk\, we discuss the paper “Large language models for scientific discovery in molecular property prediction” by Yizhen Zheng et.al.\, nature machine intelligence\, 2025. \nAbstract \nLarge language models (LLMs) are a form of artificial intelligence system encapsulating vast knowledge in the form of natural language. These systems are adept at numerous complex tasks including creative writing\, storytelling\, translation\, question-answering\, summarization and computer code generation. Although LLMs have seen initial applications in natural sciences\, their potential for driving scientific discovery remains largely unexplored. In this work\, we introduce LLM4SD\, a framework designed to harness LLMs for driving scientific discovery in molecular property prediction by synthesizing knowledge from literature and inferring knowledge from scientific data. LLMs synthesize knowledge by extracting established information from scientific literature\, such as molecular weight being key to predicting solubility. For inference\, LLMs identify patterns in molecular data\, particularly in Simplified Molecular Input Line Entry System-encoded structures\, such as halogen-containing molecules being more likely to cross the blood–brain barrier. This information is presented as interpretable knowledge\, enabling the transformation of molecules into feature vectors. By using these features with interpretable models such as random forest\, LLM4SD can outperform the current state of the art across a range of benchmark tasks for predicting molecular properties. We foresee it providing interpretable and potentially new insights\, aiding scientific discovery in molecular property prediction.
URL:https://www.ibs.re.kr/bimag/event/large-language-models-for-scientific-discovery-in-molecular-property-prediction-aqsa-awan/
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:20250627T140000
DTEND;TZID=Asia/Seoul:20250627T160000
DTSTAMP:20260423T030304
CREATED:20250426T143642Z
LAST-MODIFIED:20250609T001825Z
UID:11067-1751032800-1751040000@www.ibs.re.kr
SUMMARY:Data splitting to avoid information leakage with DataSAIL - Myna Lim
DESCRIPTION:In this talk\, we discuss the paper\, “Data splitting to avoid information leakage with DataSAIL” by Roman Joeres\, et al.\, Nature Communications\, 2025. \nAbstract \nInformation leakage is an increasingly important topic in machine learning research for biomedical applications. When information leakage happens during a model’s training\, it risks memorizing the training data instead of learning generalizable properties. This can lead to inflated performance metrics that do not reflect the actual performance at inference time. We present DataSAIL\, a versatile Python package to facilitate leakage-reduced data splitting to enable realistic evaluation of machine learning models for biological data that are intended to be applied in out-of-distribution scenarios. DataSAIL is based on formulating the problem to find leakage-reduced data splits as a combinatorial optimization problem. We prove that this problem is NP-hard and provide a scalable heuristic based on clustering and integer linear programming. Finally\, we empirically demonstrate DataSAIL’s impact on evaluating biomedical machine learning models.
URL:https://www.ibs.re.kr/bimag/event/data-splitting-to-avoid-information-leakage-with-datasail-myna-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
END:VEVENT
END:VCALENDAR