Young-Jin Park

I’m PhD Student at MIT LIDS, advised by Prof. Navid Azizan. My current research interest lies in quantifying reliability in foundational models .

I was an AI research engineer at NAVER AI Lab & CLOVA (2019-2022). At NAVER, I researched on recommender systems using LLMs and large-scale representation learning for industrial forecasting .

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 .

Selected Publications

  1. Quantifying Representation Reliability in Self-Supervised Learning Models
    Young-Jin Park, Hao Wang, Shervin Ardeshir, and Navid Azizan
    In UAI 2024
  2. A Large-Scale Ensemble Learning Framework for Demand Forecasting
    Young-Jin Park, Donghyun Kim, Frédéric Odermatt, Juho Lee, and Kyung-Min Kim
    In IEEE ICDM 2022 (Full Paper, Acceptance Rate: 9.77%)
    PDF Code
  3. Distilling a hierarchical policy for planning and control via representation and reinforcement learning
    Jung-Su Ha*, Young-Jin Park*, Hyeok-Joo Chae, Soon-Seo Park, and Han-Lim Choi
    In IEEE ICRA 2021
    PDF Code Video
  4. A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting
    Seungjae Jung*, Kyung-Min Kim*, Hanock Kwak*, and Young-Jin Park*
    In NeurIPS Workshop 2020
    🏆 Best Poster Awards
    PDF
  5. Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems
    Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, and Han-Lim Choi
    In NeurIPS 2018
    PDF Code Video
  6. Deep gaussian process-based bayesian inference for contaminant source localization
    Young-Jin Park, Piyush M Tagade, and Han-Lim Choi
    IEEE Access 2018 [IF: 4.098]
    (presented in UAI Workshop 2018 as well)
    PDF Code