Young-Jin Park

Young-Jin Park

PhD Candidate @ MIT • Graduating 2026 →

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About

I build more reliable and efficient AI systems at scale. From robotics to recommender systems to LLMs, I've consistently tackled each era's most critical challenges with cutting-edge solutions.

I'm currently pursuing my PhD at MIT while leveraging 4+ years of experience deploying billion-scale models at Meta and NAVER AI Lab. My work focuses on translating cutting-edge research into high-impact products.

Research Areas
World Modeling, End-to-End Autonomy, Large Reasoning Models, AI Safety & Alignment, Personalization
Topics
Uncertainty Quantification, Sequential Decision Making, Inference-time Scaling, Reward Modeling

Recent Posts

Stochastic Blackbox
Mar 2026 Thoughts
From Reasoning to World Models for Reliable AI
Feb 2026 Thoughts
View all posts →

Selected Publications

Full publication list available at Google Scholar

LLM Reliabiltiy
Test-Time Scaling in Clinical Decision Making: An Empirical and Analytical Investigation
J.Y. Byun, Y.J. Park, N. Azizan, and R. Chellappa
Medical Imaging with Deep Learning (MIDL), 2026
Paper
Know What You Don't Know: Uncertainty Calibration of Process Reward Models
Y.J. Park, K. Greenewald, K. Alim, H. Wang, and N. Azizan
Neural Information Processing Systems (NeurIPS), 2025 [Featured on MIT News]
Paper Project Page Slides
Robotics
Distilling a Hierarchical Policy for Planning & Control via Representation and Reinforcement Learning
J.S. Ha*, Y.J. Park*, H.J. Chae, S.S. Park, and H.L. Choi
IEEE International Conference on Robotics and Automation (ICRA), 2021
Paper
Adaptive Path-Integral Autoencoder: Representation Learning and Planning for Dynamical Systems
J.S. Ha, Y.J. Park, H.J. Chae, S.S. Park, and H.L. Choi
Neural Information Processing Systems (NeurIPS), 2018
Paper
Time-Series Forecasting
Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models
Y.J. Park, F. Germain, J. Liu, Y. Wang, T. Akino, G. Wichern, N. Azizan, C. Laughman, and A. Chakrabarty
Energy and Buildings, 2025
Paper
A Large-Scale Ensemble Learning Framework for Demand Forecasting
Y.J. Park, D. Kim, F. Odermatt, J. Lee, and K.M. Kim
IEEE International Conference on Data Mining (ICDM), 2022 [Oral Presentation; Top 9.77%]
Paper Slides