Woojae Chang
선임 연구원 at MOGAM Institute for Biomedical Research
Seoul, South Korea
Summary
👤
Senior🎓
Top SchoolWoojae Chang is a Professional AI Scientist based in Seoul with 10 years of experience applying deep learning to drug discovery and precision medicine. He specializes in transformers, multimodal and molecular representation learning, and building large-scale, distributed training pipelines that emphasize interpretability and real-world impact. His work spans academic and industry settings—from Samsung Advanced Institute of Technology and Standigm to his current role at Gradiant Bioconvergence—and includes publications on domain adaptation, graph transformers, and quantum-informed molecular representations. Comfortable at the intersection of biology and AI, he leverages self-supervised, transfer, and multi-task learning to tackle noisy, heterogeneous biomedical data. He changed his name from Woong-gi Chang in 2023, so some prior work may appear under that name.
10 years of coding experience
5 years of employment as a software developer
Master's degree, Computer Science and Engineering, Master's degree, Computer Science and Engineering at Pohang University of Science and TechnologyNanodegree, Computer Science, Nanodegree, Computer Science at UdacityHong Kong University of Science and Technology (HKUST)
English, Korean, Japanese
Github Skills (31)
python10
machine-learning10
numpy10
deep-learning10
gpu10
neural-network10
batch-normalization9
autograd9
adversarial-learning9
tensor9
gpu-acceleration9
few-shot-learning8
multi-task-learning7
generative-adversarial-network7
adaptation7
Programming languages (3)
Github contributions (5)
woozch/DSBN
Nov 2018 - May 2020
Official Implementation of "Domain Specific Batch Normalization for Unsupervised Domain Adaptation (CVPR2019)"
Contributions:3 commits, 1 PR, 3 pushes in 1 year 6 months
batch-normalization
woozch/AGDLDM
Aug 2025 - Aug 2025
Contributions:11 pushes, 1 branch in 20 days
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