- Computer Science & Engineering @ Korea University (Mar 2022 - Present)
- Brain and Cognitive Sciences @ Korea University (Mar 2024 - Present)
- Exchange Student @ The University of Texas at Austin (Jan 2025 - May 2025)
Undergraduate Researcher, Data & Adaptive Intelligence Systems Lab (Advisor: Prof. Susik Yoon), Korea University
July 2024 – Dec 2025
- Conducted research on time-series representation learning with large language models (LLMs).
Numerical-Token-Grounded Time-Series & Textual Embedding Alignment for Forecasting
July 2025 – Dec 2025
(Individually led research conducted at Data & Adaptive Intelligence Systems Lab under the supervision of Prof. Susik Yoon, Korea University)
- Identified that existing time-series–text methods rely on naive channel-wise attention without explicit alignment criteria.
- Proposed a numerical token–based alignment framework by converting time-series values into natural-language descriptions.
- Aligned timestamp-level time-series embeddings with number-aware textual token embeddings, enabling fine-grained and interpretable cross-modal representations.
- Validated the framework through basic experiments on ETTh1 and ILI datasets.
- Manuscript in preparation.
- Interpretable representation learning
- Multimodal representation learning
- Brain-inspired AI
- Scene understanding & structured representation (e.g., scene graph generation)
- Autonomous driving perception
- 📨 Email: [email protected]
- 🌐 Google Scholar / CV: (coming soon)
✨ “Keep it simple, but meaningful”