Chi-Nguyen Tran

I’m a final-year B.Sc. student in Artificial Intelligence at VNUHCM, University of Science (HCMUS), and AI R&D Lead at Realtime Robotics.

I am doing research in robotics. My goal is to help robots understand what is around them, know where they are, and work out how to act, and whether it is safe to act, when their surroundings change. In the real world, light, weather and surroundings keep changing and sensors get noisy, so a model that scores well on test data can still fail once deployed. I work on models that hold up in those conditions and are light enough to run on the small computers robots carry:

  • Perceive and localize. Robots that keep recognising where they are when the weather and the scene change.
  • Act. Agents that act reliably on camera input distorted in ways they never saw in training.
  • Test. Finding the road scenarios most likely to break a self-driving car.
  • Next. Teams of robots that learn whom to trust, and vision-language-action models for acting on what they see.

I am looking for PhD positions starting Fall 2027 in robotics.

Chi-Nguyen Tran

News

  • 5 papers accepted at NeurIPS 2026 (Sydney), 2 as co-first author.
  • Awarded the Vallet Scholarship 2026.
  • Winner, FSE-AIWare 2026 Agentic Python Dependency Resolution Competition (MEMRES).
  • Joined Realtime Robotics as AI R&D Lead, working on perception and localization for robots on embedded hardware.
  • 1st Place in both tracks, NeurIPS 2025 Mouse vs. AI Robust Foraging Competition.

Selected Publications

* equal contribution

MEMRES: A Memory-Augmented Resolver with Confidence Cascade for Agentic Python Dependency Resolution
FSE 2026 AIWare Competition Co-first author
Chi-Nguyen Tran*, Duy-Minh Dao-Sy*, Trung-Kiet Huynh*, Phu-Hoa Pham, Phu-Quy Nguyen-Lam, Vu Nguyen
Winner, FSE-AIWare 2026 Agentic Python Dependency Resolution Competition
An agentic resolver that repairs broken Python environments with a self-evolving memory, a confidence cascade over version candidates, and a build-and-reflect loop.
MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound
NeurIPS 2026 Main Track Co-first author
Phu-Hoa Pham*, Chi-Nguyen Tran*, Phu-Quy Nguyen-Lam, Duy-Minh Dao-Sy, Trung-Kiet Huynh, Long Tran-Thanh
Streaming decision trees that learn new classes online from a data stream, with split decisions backed by a McDiarmid concentration bound and knowledge inherited across splits.
Unlocking Compositional Generalization in Continual Few-Shot Learning
NeurIPS 2026 Main Track
Phu-Quy Nguyen-Lam, Phu-Hoa Pham, Duy-Minh Dao-Sy, Chi-Nguyen Tran, Trung-Kiet Huynh, Long Tran-Thanh
Holistic training with a slot-attention router, then compositional inference that scores queries part by part, so a continual few-shot learner generalizes to unseen combinations of known parts.
Training-Free Cultural Alignment of Large Language Models via Persona Disagreement
NeurIPS 2026 Main Track
Trung-Kiet Huynh, Duy-Minh Dao-Sy, Tuan Nguyen, Chi-Nguyen Tran, Phu-Hoa Pham, Phu-Quy Nguyen-Lam, The Anh Han, Long Tran-Thanh
Aligns a frozen LLM to the moral preferences of a country without training: country-conditioned personas vote, and only the decisions they agree on are kept.
Visual Robustness and Neural Alignment in a Shared Foraging Task: The Mouse vs. AI Benchmark
NeurIPS 2026 Evaluations & Datasets Track
Marius Schneider, Joe S. Canzano, Yuchen Hou, Jing Peng, A. Deepu, U. Karan, Phu-Hoa Pham, Chi-Nguyen Tran, Duy-Minh Dao-Sy, Phu-Quy Nguyen-Lam, Trung-Kiet Huynh, S. Azeglio, Spencer L. Smith, Michael Beyeler
The benchmark paper of the NeurIPS 2025 Mouse vs. AI competition, co-authored with the organizers as a member of the first-place team.

All publications →

Honors

  • 1st Place (both tracks), NeurIPS 2025 Mouse vs. AI Robust Foraging Competition
  • Winner, FSE-AIWare 2026 Agentic Python Dependency Resolution Competition
  • 1st Place in five shared tasks at SemEval-2026, LREC 2026 and EACL 2026
  • Third Prize, Vietnam Student AI Olympiad, National Round 2025
  • Vallet Scholarship 2026

Service

  • Reviewer: ACL, CVPR Workshops