(Cancelled) TBD – Amir Sharafkhaneh
ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)-
9 events found.
Seminar
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Generative Models and Causality – Kyungwoo Song
B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic ofThis 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 …
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Modeling personalized heart rate response to exercise and environmental factors with wearables data – Dongju Lim
B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic ofIn 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. Abstract Heart …
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Empirical modeling of bifurcations and chaos from time series – Stephan Munch
ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)Abstract Many natural systems exhibit complex dynamics and are prone to sudden changes or ‘regime shifts’. At the same time, many of these systems are sparsely observed posing considerable challenges …
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Quantifying interventional causality by knockoff operation – Yun Min Song
B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic ofIn this talk, we discuss the paper "Causal disentanglement for single-cell representations and controllable counterfactual generation" by Yicheng Gao et al., Nature Communications, 2025. Abstract Conducting disentanglement learning on single-cell …
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N-BEATS: Neural basis expansion analysis for interpretable time series forecasting – Jinwoo Hyun
B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic ofIn this talk, we discuss the paper "N-BEATS: Neural basis expansion analysis for interpretable time series forecasting" by B. Oreshkin et al., ICLR, 2020. Abstract We focus on solving the …
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Distribution shift in machine learning: robustness, invariance, and a causal view – Wooseok Ha
B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic ofClassical machine learning models are typically trained under the assumption that the training (source) and test (target) data are drawn from the same distribution. However, real-world data are rarely clean …
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Expanding the Data Analysis Toolkit: Explainable AI, Causal Learning, and Time-Series Foundation Models – Daeil Jang
B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic ofRecent advances in data science have expanded the scope of data analysis beyond prediction accuracy toward interpretability, causal understanding, and generalizable learning across complex data structures. This lecture introduces three …
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Rationalizing Therapeutics: Mathematical Insights into Drug and Cell Therapy Development – Seokjoo Chae
B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic ofTBA