Minjae Kwon

Charlottesville, VA

I am a PhD candidate in the Department of Computer Science at the University of Virginia, advised by Prof. Lu Feng. Previously, I received a PhD in mathematics from Kyungpook National University.

My research asks one question: how can learning systems adapt to changing environments while maintaining safety at runtime? Training-time safety guarantees can break when deployment conditions differ from training. My work addresses this problem in three directions:

Adaptive runtime safety — safety wrappers that infer changes in the dynamics at deployment and adjust their safety margin under uncertainty, using basis-adaptive neural ODEs and conformal prediction without retraining the policy.
Safe in-context adaptation — frozen policies that adapt their strategy from interaction history while respecting a safety budget, using methods such as safe algorithm distillation and latent Q-barrier filtering.
Compositional safe adaptation — agents that execute unseen compositions of temporally structured tasks (specified in linear temporal logic) under distribution shift.

I study these problems in safety-critical domains, including robotic control in MuJoCo simulation environments and healthcare applications such as personalized diabetes management using physiological simulators.

news

Jul 2026	Invited talk at the Korea Institute for Advanced Study (KIAS): “Safe Decision-Making When the World Changes”.
May 2026	Two papers accepted to ICML 2026: Safe In-Context Reinforcement Learning and Safety Generalization Under Distribution Shift in Safe RL: A Diabetes Testbed.
May 2026	Received the ICML 2026 Gold Reviewer Award.
Jul 2025	Received a UAI 2025 conference scholarship.
May 2025	Paper accepted to UAI 2025: Adaptive Reward Design for Reinforcement Learning.

selected publications

* Equal contribution.

ICML

Safe In-Context Reinforcement Learning

Amir Moeini^*, Minjae Kwon^*, Alper Kamil Bozkurt, Yuichi Motai, Rohan Chandra, Lu Feng, and Shangtong Zhang

International Conference on Machine Learning (ICML), 2026

Bib PDF

@inproceedings{moeini2026safeincontext,
  title = {Safe In-Context Reinforcement Learning},
  author = {Moeini, Amir and Kwon, Minjae and Bozkurt, Alper Kamil and Motai, Yuichi and Chandra, Rohan and Feng, Lu and Zhang, Shangtong},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
}

ICML

Safety Generalization Under Distribution Shift in Safe Reinforcement Learning: A Diabetes Testbed

Minjae Kwon, Josephine Lamp, and Lu Feng

International Conference on Machine Learning (ICML), 2026

Bib PDF

@inproceedings{kwon2026safetygeneralization,
  title = {Safety Generalization Under Distribution Shift in Safe Reinforcement Learning: A Diabetes Testbed},
  author = {Kwon, Minjae and Lamp, Josephine and Feng, Lu},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
}

Preprint

Latent Q-Barrier Shielding for Safe In-Context Reinforcement Learning

Minjae Kwon^*, Amir Moeini^*, Shangtong Zhang, and Lu Feng

Preprint, 2026

arXiv Bib PDF

@article{kwon2026safeicrllatentbarrier,
  title = {Latent Q-Barrier Shielding for Safe In-Context Reinforcement Learning},
  author = {Kwon, Minjae and Moeini, Amir and Zhang, Shangtong and Feng, Lu},
  year = {2026},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},
}

Preprint

Adaptive Shielding for Safe Reinforcement Learning under Hidden-Parameter Dynamics Shifts

Minjae Kwon, Tyler Ingebrand, Ufuk Topcu, and Lu Feng

Preprint, 2026

arXiv Bib PDF

@article{kwon2026adaptiveshielding,
  title = {Adaptive Shielding for Safe Reinforcement Learning under Hidden-Parameter Dynamics Shifts},
  author = {Kwon, Minjae and Ingebrand, Tyler and Topcu, Ufuk and Feng, Lu},
  year = {2026},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},
}

UAI

Adaptive Reward Design for Reinforcement Learning

Minjae Kwon, Ingy ElSayed-Aly, and Lu Feng

Conference on Uncertainty in Artificial Intelligence (UAI), 2025

Reward design for linear temporal logic specifications

Bib PDF

@inproceedings{kwon2025adaptiverewarddesign,
  title = {Adaptive Reward Design for Reinforcement Learning},
  author = {Kwon, Minjae and ElSayed-Aly, Ingy and Feng, Lu},
  booktitle = {Conference on Uncertainty in Artificial Intelligence (UAI)},
  year = {2025},
  note = {Reward design for linear temporal logic specifications},
}