Name: Reservoir Computing: Machine Learning Meets Nonlinear Dynamics – Ying-Cheng Lai
Start: 2026-10-02T11:00:00+09:00
End: 2026-10-02T12:00:00+09:00
Location: B232 Seminar Room, IBS

S	M	T	W	T	F	S
		0 events 1 0 events, 1	0 events 2 0 events, 2	0 events 3 0 events, 3	1 event 4 1 event, 4 11:00 am - 12:00 pm Design of Single-Cell Experiments to Efficiently Infer Predictive Mechanistic Models for Stochastic Gene Expression – Brian Munsky September 4 @ 11:00 am - 12:00 pm KST Design of Single-Cell Experiments to Efficiently Infer Predictive Mechanistic Models for Stochastic Gene Expression – Brian Munsky Biochemical assays have made outstanding progress to elucidate how cells sense and respond to stimuli, but mechanistic and parametric uncertainties preclude quantitative predictions for the full spatial, temporal, and heterogeneous responses of signal-activated gene expression. Stochastic models use random noise as an abstraction to account for these unknown or uncertain dynamics. When inferred from appropriate … Continue Reading	0 events 5 0 events, 5
0 events 6 0 events, 6	0 events 7 0 events, 7	0 events 8 0 events, 8	0 events 9 0 events, 9	0 events 10 0 events, 10	1 event 11 1 event, 11 10:00 am - 12:00 pm Identification of predictive subphenotypes for clinical outcomes using real world data and machine learning – Myna Lim September 11 @ 10:00 am - 12:00 pm KST Identification of predictive subphenotypes for clinical outcomes using real world data and machine learning – Myna Lim In this Journal club, we will discuss the paper "Identification of predictive subphenotypes for clinical outcomes using real world data and machine learning", Weishen Pan et al., Nature Communications, 2025. Abstract: Predicting treatment response is an important problem in real-world applications, where the heterogeneity of the treatment response remains a significant challenge in practice. Unsupervised … Continue Reading	0 events 12 0 events, 12
0 events 13 0 events, 13	0 events 14 0 events, 14	0 events 15 0 events, 15	0 events 16 0 events, 16	0 events 17 0 events, 17	1 event 18 1 event, 18 10:00 am - 12:00 pm Learning to learn ecosystems from limited data – Hyeong Jun Jang September 18 @ 10:00 am - 12:00 pm KST Learning to learn ecosystems from limited data – Hyeong Jun Jang In this talk, we discuss the paper "Learning to learn ecosystems from limited data" by Zheng-Meng Zhai et al., PNAS, 2025. Abstract: A fundamental challenge in developing data-driven approaches to ecological systems for tasks such as state estimation and prediction is the paucity of the observational or measurement data. For example, modern machine-learning techniques such … Continue Reading	0 events 19 0 events, 19
0 events 20 0 events, 20	0 events 21 0 events, 21	0 events 22 0 events, 22	0 events 23 0 events, 23	0 events 24 0 events, 24	0 events 25 0 events, 25	0 events 26 0 events, 26
0 events 27 0 events, 27	0 events 28 0 events, 28	0 events 29 0 events, 29	0 events 30 0 events, 30