Dongsu Lee
Hello! I am a PhD student at the University of Texas at Austin,
where I'm supervised by Prof. Amy Zhang in the
MIDI Lab.
Previous affiliation.
I collaborated with Prof. Ding Zhao in the
Safe AI Lab,
Carnegie Mellon University (CMU); with Prof. Minhae Kwon in the
BMIL; and Prof. Sangsoo Kim in the
Post-genome Informatics Lab at Soongsil University in Seoul, South Korea.
The research question I focus on is simple these days: Will reinforcement learning (RL) methods be applicable for practical deployment in realistic settings?
I’m interested in real-world challenges, e.g, partial observability, coordination with unseen partners, and human-robot collaboration.
Then, I aim to make an RL algorithm applicable in multi-agent, open-ended, and robotics settings via both self-play and data-driven techniques.
So, I work on multi-agent RL, representation learning, and offline-online RL.
My guiding ethos.
Speak truth, amid echoing scorn.
Embrace adventure, born of conviction.
Take responsibility, without retreat.
Email /
CV /
Scholar /
LinkedIn /
Github
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Scenario-free Autonomous Driving with Multi-task Offline-to-online Reinforcement Learning
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Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning
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Episodic Future Thinking With Offline Reinforcement Learning for Autonomous Driving
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Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning
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AD4RL: Autonomous Driving Benchmarks for Offline Reinforcement Learning with Value-based Dataset
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Instant Inverse Modeling of Stochastic Driving Behavior with Deep Reinforcement Learning
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Stability Analysis in Mixed-autonomous Traffic with Deep Reinforcement Learning
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ADAS-RL: Safety Learning Approach for Stable Autonomous Driving
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Apparatus and Method for Inferring Driving Characteristics of Vehicle
Dongsu Lee,
Minhae Kwon,
Oct 4, 2022. (Application no. US 17/959515), issued on June X, 2025. (Patent no. US X)
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Method for Combating Stop-and-Go Wave Problem Using Deep Reinforcement Learning based Autonomous Vehicles, Recording medium and device for performing this the method
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Scholarships
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AI Intensive Program at Carnegie Mellon University (Aug 2024 - Feb 2025)
IITP & Sogang University
(USD 41K)
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Future Industrial Talent Scholarship (Aug 2023 - Feb 2025)
CMK Hyundai Group
(Full tuition + KRW 3,600K / year)
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Awards
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Global Excellence Scholarship (Prize: KRW 3,000K), CMK Hyundai Group (Mar, Dec, Dec 2024)
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ICT Challenge IITP President’s Award (Prize: KRW 5,000K), IITP (Sep 2023)
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Reviews
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Journals:
Nature Communication (2025),
IEEE T-ITS (2025),
IEEE T-CE (2025),
IEEE T-VT (2023),
IEEE IoT-J (2023)
Conferences:
NeurIPS (2025),
CoRL (2025),
IRoS (2025),
ICRA (2025),
ICML (2022)
Workshops:
ICML Theory of Mind Workshop (2023; Program committee),
NeurIPS ML4AD (2022)
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