Headed an independent research team focused on multi-objective molecule generation, tackling a wide range of structures like polymers and drug-like molecules. Beyond my research, I offered machine learning consultation to internal domain experts, enhancing their projects with technical insights.
Teaching Assistant
2023.09 — 2023.12, 2024.03 — 2024.06
Machine Learning for Artificial Intelligence (KAIST)
Tackled efficient domain adaptation for point clouds, critical due to distributional shifts in real-world applications, by developing GNN-based topology-aware adapters to seamlessly adapt models from source data to target local characteristics.
Developed a pseudo-labeling approach for graph edge prediction task, assigning labels to disconnected node pairs using a hierarchical scoring mechanism.