About Me
Hi! I’m an MSc student at KAIST AI, advised by Seong Joon Oh. I’m broadly interested in how LLMs can stay reliable and personalized as the knowledge they depend on keeps changing. Specifically, my research interests center on:
- Retrieval-augmented generation (RAG) and retrieval methods
- Knowledge editing through RAG, keeping models’ factual knowledge up to date
- Building memory systems and personalization for long-term, persistent agents
Before my graduate studies, I spent six years co-founding and building a deep learning startup, working end-to-end across business, research, and engineering. That experience shaped how I think about research — not just as a technical problem, but as something that has to hold up in the messy, real-world systems where people actually rely on it.
In short, I’d like to build LLM-based systems that can remember, retrieve, and update what they know in a way that is both trustworthy and personalized to the people who use them.
🔥 Recent News
- [May 2026] Paper accepted at the ICML 2026 Workshop on Scalable Learning and Optimization for Efficient Multimodal AI Agents (SCALE)!
- [May 2026] Paper accepted at the ICML 2026 Workshop on the Impact of Memorization on Trustworthy Foundation Models!
- [May 2026] Released MEME: Multi-Entity & Evolving Memory Evaluation as a preprint on arXiv.
- [Feb 2026] Joined the Scalable Trustworthy AI (STAI) group at KAIST AI.
- [May 2019] Paper accepted at ICASSP 2019.
📝 Selected Publications
- MEME: Multi-Entity & Evolving Memory Evaluation Paper Code Project PageICML 2026 Workshop on Scalable Learning and Optimization for Efficient Multimodal AI Agents (SCALE)
- Break the Output Geometry for Large Language Model UnlearningICML 2026 Workshop on the Impact of Memorization on Trustworthy Foundation Models
- Polyphonic Sound Event Detection Using Convolutional Bidirectional LSTM and Synthetic Data-based Transfer Learning PaperICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)