AI Research Intern at Polymerize

2024.10 - 2025.04

• 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)

Research Intern at Machine Learning and Intelligence Lab (MLILAB), KAIST

2022.04 - 2023.02

• Advisor: Prof. Eunho Yang, Mentor: Joonhyung Park (Ph.D. Candidate, 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.

Research Intern at Data Mining and Information Systems Lab (DMIS), Korea University

2021.01 - 2022.02

• Advisor: Prof. Jaewoo Kang, Mentor: Seongjun Yun (Applied Scientist, Amazon)

• Developed a pseudo-labeling approach for graph edge prediction task, assigning labels to disconnected node pairs using a hierarchical scoring mechanism.

Invited Talks

• Learning Flexible Forward Trajectories for Masked Molecular Diffusion
  • LG Materials Intelligence Lab (Seoul, South Korea)

Conference Reviewer

• International Conference on Learning Representations (ICLR)
• Neural Information Processing Systems (NeurIPS)