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

profile photo
Write-up
Blog Fast and expressive multi‑agent coordination (Oct 2025)
Capturing complex joint behavior while keeping inference fast.
Blog Research statement: Towards generalizable MARL (Aug 2025)
Control’s bottlenecks and a roadmap for generalizable MARL.
Publications
Multi-agent Coordination via Flow Matching
Learning to Interact in World Latent for Team Coordination
Dongsu Lee, Daehee Lee, Yaru Niu, Honguk Woo, Amy Zhang, Ding Zhao
Preprint
NeurIPS 2025 ARLET Workshop (Oral)
paper | code | project page
Policy Compatible Skill Incremental Learning via Lazy Learning Interface
Daehee Lee, Dongsu Lee, TaeYoon Kwack, Wonje Choi, Honguk Woo
NeurIPS 2025 (Spotlight)
paper | code
Scenario-free Autonomous Driving with Multi-task Offline-to-online Reinforcement Learning
Dongsu Lee, Minhae Kwon
IEEE Transactions on Intelligent Transportation Systems
paper | code
Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning
AD4RL: Autonomous Driving Benchmarks for Offline Reinforcement Learning with Value-based Dataset
Patents
Method and Apparatus for Inferring Stochastic Driving Characteristic of a Vehicle
Dongsu Lee, Minhae Kwon, Apr 26, 2023. (Application no. US 18/139602), issued on December XX, 2025. (Patent no. US XX)
Apparatus and Method for Inferring Driving Characteristics of Vehicle
Dongsu Lee, Minhae Kwon, Oct 4, 2022. (Application no. US 17/959515), issued on Aug 19, 2025. (Patent no. US 12,391,261)
Method for Combating Stop-and-Go Wave Problem Using Deep Reinforcement Learning based Autonomous Vehicles, Recording medium and device for performing this the method
Dongsu Lee, Minhae Kwon, Patent no. US 12,091,025 (Sep 2024)
Honors and Awards

Scholarships

UT Austin Engineering Fellowship (Aug 2025 - Present)

Cockrell School of Engineering and The University of Texas at Austin Graduate School (USD 6K / year)

AI Intensive Program at Carnegie Mellon University (Aug 2024 - Feb 2025)

IITP & Sogang University (USD 41K)

Future Industrial Talent Scholarship (Aug 2023 - Feb 2025)

CMK Hyundai Group (Full tuition + KRW 3,600K / year)

Awards

Global Excellence Scholarship (Prize: KRW 3,000K), CMK Hyundai Group (Mar, Dec, Dec 2024)

ICT Challenge IITP President’s Award (Prize: KRW 5,000K), IITP (Sep 2023)

Services

Reviews

Journals: Nature Communication, IEEE T-ITS, IEEE T-CE, IEEE T-VT, IEEE IoT-J

Conferences: ICLR (2026), NeurIPS (2025), CoRL (2025), IRoS (2025), ICRA (2026, 2025), ICML (2022)

Workshops: ICML Theory of Mind Workshop (2023; Program committee), NeurIPS ML4AD (2022)


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