Woojin Cho
I am currently an AI researcher at TelePIX (Alternative military service: Technical research personnel). I completed my M.S. degree advised by Prof.Noseong Park (Bigdata Analytics Lab) in the Department of Artificial Intelligence at Yonsei University and continue to collaborate closely on research. My primary research areas include scientific machine learning, foundation model, implicit neural representations (INR) and satellite technology. Additionally, I have an interest in deep learning frameworks based on meta-learning, pruning and data compression method. In the future, I aspire to research related to simulation techniques by combining numerical analysis methods with scientific ML technologies (e.g., Physics-informed neural networks, Neural operator, etc.). I have a goal to build an artificial intelligence that can be interpreted mathematically and statistically. I greatly enjoy collaborating with researchers who share similar interests. If you are interested in my research areas or would like to collaborate, please feel free to contact me! (snowmoon@yonsei.ac.kr)
Publication
Unveiling the Potential of Superexpressive Neural Networks in Implicit Neural Representations
U Mudiyanselage, W Cho, M Jo, N Park, K Lee
(Under review)
MaD-Scientist: AI-based Scientist solving Convection-Diffusion-Reaction Equations Using Massive PINN-Based Prior Data
M Kang, D Lee, W Cho, J Park, K Lee, A Gruber, Y Hong, N Park
(Under review)
[Paper]
FastLRNR and Sparse Physics Informed Backpropagation
W Cho, K Lee, N Park, D Rim, G Welper
(Under review)
[Paper]
NeRT: Implicit Neural Representation for Time Series
W Cho*, M Jo*, K Lee, N Park
(Under review)
[Paper]
Can we pre-train ICL-based SFMs for the zero-shot inference of the 1D CDR problem with noisy data?
M Kang, D Lee, W Cho, K Lee, A Gruber, N Trask, Y Hong, N Park
NeurIPS 2024 workshop
Neural Compression for Multispectral Satellite Images
W Cho*, S Immanuel*, J Heo, D Kwon
NeurIPS 2024 workshop
[Code]
[Project]
Promoting Sparsity In Continuous-Time Models To Learn Delayed Dependence Structures
F Wu, W Cho, D Korotky, S Hong, D Rim, N Park, K Lee
CIKM 2024
[Paper]
Parameterized Physics-informed Neural Networks for Solving Parameterized PDEs
W Cho, M Jo, H Lim, K Lee, D Lee, S Hong, N Park
ICML 2024 (Oral, Top 1.52%)
[Paper]
[Code]
[Presentation]
Extension of Physics-informed Neural Networks to Solving Parameterized PDEs
W Cho, M Jo, H Lim, K Lee, D Lee, S Hong, N Park
ICLR 2024 workshop
[Paper]
[Code]
Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer
Y Yu, J Choi, W Cho, K Lee, N Kim, K Chang, C Woo, I Kim, S Lee, J Yang, S Yoon, N Park
ICLR 2024
[Paper]
[Code]
Operator-learning-inspired Modeling of Neural Ordinary Differential Equations
W Cho*, S Cho*, H Jin, J Jeon, K Lee, S Hong, D Lee, J Choi, N Park
AAAI 2024
[Paper]
[Code]
Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks
W Cho, K Lee, D Rim, N Park
NeurIPS 2023 (Spotlight, Top 3.06%)
[Paper]
[Code]
Education
- Yonsei University
M.S in Artificial Intelligence
- Yonsei University
B.S in Atmospheric science
B.S in Electrical electronic engineering - Sejong Science High School
Career
- Telepix ( Jun 2024 - Present )
AI research team (Alternative military service)
- Arizona State University ( Jan 2024 - Jun 2024 )
Visiting Researcher (hosted by Prof.Kookjin Lee)
Invited Talk
- Scientific Machine Learning (hosted by KIAS)
- Parameterized Physics-informed Neural Networks for Parameterized PDEs (hosted by ML2)
- Latest Trends in Machine Learning based Physics Simulation (hosted by Samsung Electronics)
- Physics-informed Neural Networks for Solving PDEs (hosted by Alsemy)