Built benchmarks and a large-scale training corpus for protein design and broader biology, gaining hands-on experience in LLM mid- and post-training. Also developed an agent framework that unifies protein design tools for autonomous end-to-end workflows.
Headed an independent research team focused on multi-objective molecule generation, tackling a wide range of structures like polymers and drug-like molecules.
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.