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
Large Language Models, Agentic Enterprise, AI Safety & Alignment, Personalization, Planning & Control
Topics
Inference-time Scaling, Reward Modeling, Uncertainty Quantification, Sequential Decision Making

News

Sep 2025
Paper on "Uncertainty Calibration of Process Reward Models" accepted to NeurIPS 2025
Aug 2025
Completed internship at Meta working on LLM features for Instagram ads
July 2024
Received Wunsch Foundation Award for excellence in graduate student research at MIT

Selected Publications

Full publication list available at Google Scholar

2025
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
Paper Slides
Test-Time-Scaling for Zero-Shot Diagnosis with Visual-Language Reasoning
J.Y. Byun, Y.J. Park, N. Azizan, and R. Chellappa
IEEE Winter Conference on Applications of Computer Vision (WACV), submitted
Paper
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
2024
Quantifying Representation Reliability in Self-Supervised Learning Models
Y.J. Park, H. Wang, S. Ardeshir, and N. Azizan
Conference on Uncertainty in Artificial Intelligence (UAI), 2024 [Spotlight @ 2023 RSS Workshop]
Paper Slides Poster
2022
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
2021
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