I was an AI research engineer at NAVER AI Lab & CLOVA. At NAVER, I developed a user behavior models and recommender systems using large language models (LLMs) for tens of millions of users. I also conducted research on large-scale representation learning for temporal data (e.g., demand forecasting systems).
I received my M.S. and B.S. degree in Aerospace Engineering and Mathematical Sciences (minor) from KAIST in 2019 and 2017, respectively, under the supervision of Prof. Han-Lim Choi. During my master’s degree, I researched 1) hierarchical RL and 2) deep latent variable models , aiming to bridge the gap between high-level reasoning and low-level control policies.
- Representation Reliability and Its Impact on Downstream TasksarXiv preprint 2023
- A Large-Scale Ensemble Learning Framework for Demand ForecastingIn IEEE ICDM 2022 (Full Paper, Acceptance Rate: 9.77%)
- Distilling a hierarchical policy for planning and control via representation and reinforcement learningIn IEEE ICRA 2021
- A Worrying Analysis of Probabilistic Time-series Models for Sales ForecastingIn NeurIPS Workshop 2020
🏆 Best Poster Awards
- Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical SystemsIn NeurIPS 2018
- Deep gaussian process-based bayesian inference for contaminant source localizationIEEE Access 2018 [IF: 4.098](presented in UAI Workshop 2018 as well)