KIAS Center for AI and Natural Sciences 2024 Fall Workshop

KIAS Center for AI and Natural Sciences Fall Workshop

November 5 - 8, 2024 Nest Hotel, Incheon

Program

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Day 1 – Nov. 5 (Tue)

8:55 - 9:00	Opening Remarks
9:00 – 10:00	Tutorial- Krikamol Muandet	Causal Machine Learning
10:00 - 10:15	Break
10:15 - 10:55	Daniel D. Lee	Fluid Dynamic Models in Machine Learning
10:55 - 11:15	Thomas Dieter Flacke
11:15 - 11:30	Break
11:30 - 12:10	Jonggeol Na
12:10 - 14:00	Lunch
14:00 - 14:20	Jakwang Kim	Adversarial robustness in classification via the lens of optimal transport
14:20 - 14:40	Jaewoong Choi	Improving Neural Optimal Transport via Displacement Interpolation
14:40 - 15:00	Break
15:00 - 15:40	Krikamol Muandet	On Imprecise Generalisation: From Invariance to Heterogeneity
15:40 - 18:00	Poster session
18:00 – 20:00	Dinner

Day 2 – Nov. 6 (Wed)

9:00 – 10:00	Tutorial- Yung-Kyun Noh
10:00 - 10:15	Break
10:15 - 10:55	Hsuan-Tien Lin
10:55 - 11:15	Ji Woong Yu	Recent Application of Machine Learning Force Field: Beyond Simple Molecular Dynamics Simulation
11:15 - 11:30	Break
11:30 - 12:10	Chieh-Hsin Lai	Enhancing Accuracy and Efficiency in Diffusion Models
12:10 - 14:00	Lunch
14:00 - 14:20	Yeachan Park	Understanding and acceleration of grokking phenomena in learning arithmetic operations via Kolmogorov-Arnold representation
14:20 - 15:00	Jaeyong Lee	Real-Time Solutions to PDEs with Neural Operators in Scientific Machine Learning
15:00 - 15:20	Break
15:20 - 15:40	Jun Sur Park	WGFINNs: Weak-form Generic formalism informed neural networks
15:40 - 16:00	Changhoon Song	How does PDE order affect the convergence of PINNs?
16:00 - 16:20	Kwang Hyun Cho	Automized dataset collection for PES interpolation for pigment-protein complexes
16:20 - 16:40	Break
16:40 - 17:20	Yung-Kyun Noh
17:20 - 18:00
18:00 – 20:00	Banquet

Day 3 – Nov. 7 (Thur)

9:00 – 10:00	Tutorial- Masashi Sugiyama	Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach
10:00 - 10:15	Break
10:15 - 10:55	Guido Montúfar
10:55 - 11:15	Daeseong Yong	Dynamic Programming for Chain Propagator Computations in Polymer Field Theory Simulations
11:15 - 11:30	Break
11:30 - 12:10	Masashi Sugiyama	Machine Learning under Distribution Shifts
12:10 - 14:00	Lunch
14:40 - 18:00	Excursion
18:00 – 20:00	Dinner

Day 4 – Nov. 8 (Fri)

9:00 – 9:40	Chulhee Yun	Provable Benefit of Cutout and CutMix for Feature Learning
9:40 - 10:20	Seungchan Ko	Finite Element Operator Network: Theory and Computation
10:20 - 10:35	Break
10:35 - 11:15	Kyungwoo Song	Robust Machine Learning via Sufficient Invariant and Causal-Aware In-Context Learning
11:15 - 11:35	Sangwoong Yoon	Value Gradient Sampling
11:35 - 11:55	Break
11:55 - 12:35	Dohyun Kwon
12:35 - 14:00	Lunch
14:00 -	Closing

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