Shuaijun Liu (George)
Portrait

I am a Ph.D. student at The Hong Kong University of Science and Technology (Guangzhou), supervised by Prof. Ningxin Su in NEBULIS Lab.

My research focuses on Embodied Intelligence, Vision-Language-Action Models, Robotic Manipulation, World Models, Reliable Decision-Making, and Efficient Inference for autonomous agents in physical environments. I am especially interested in Distributed Model Deployment, Replanning, and Multi-Agent Collaboration for embodied and autonomous systems.

GitHub ORCID
Education
  • The Hong Kong University of Science and Technology (Guangzhou)
    The Hong Kong University of Science and Technology (Guangzhou)
    Sep. 2025 - Jun. 2028
  • Boston University
    Boston University
    M.S. in Computer Science
    Sep. 2023 - May 2025
  • Hong Kong Baptist University
    Hong Kong Baptist University
    B.S. in Statistics and Data Science
    Minor in Computer Science
    Sep. 2019 - Jun. 2023
Experience
  • Amazon
    Applied Scientist Intern, New York
    Jul. 2024 - Oct. 2024
Honors & Awards
  • China Scholarship Council (CSC) Full Scholarship for Ph.D. Study
    2025
  • Outstanding Graduation Thesis
    2023
  • National Second Prize and Guangdong First Prize, China Undergraduate Mathematical Contest in Modeling
    2022
  • Meritorious Winner (First Prize), Mathematical Contest in Modeling (U.S. undergraduate competition)
    2022
News
2026
May 01
2025
May 26
Completed M.S. study in Computer Science at Boston University.
May 01
Selected Publications (view all )
Task-semantic action calibration for vision-language-action models

Shuaijun Liu, Ningxin Su#

Manuscript under review 2026

Studies action reliability under appearance shifts and task-semantic changes, aiming to preserve stable behavior under semantic invariance while separating actions correctly when goals, constraints, or order change.

Task-semantic action calibration for vision-language-action models

Shuaijun Liu, Ningxin Su#

Manuscript under review 2026

Studies action reliability under appearance shifts and task-semantic changes, aiming to preserve stable behavior under semantic invariance while separating actions correctly when goals, constraints, or order change.

When Replanning Becomes the Bottleneck: Budgeted Replanning for Embodied Agents

Shuaijun Liu, Feiyang You, Xingwei Chen, Ningxin Su#

International Conference on Machine Learning (ICML) 2026 Main (Regular)

Studies replanning overhead in long-horizon embodied agents and introduces a budgeted replanning framework with adaptive replanning decisions, token budgets, latency SLOs, and context compression.

[Project Website]

When Replanning Becomes the Bottleneck: Budgeted Replanning for Embodied Agents

Shuaijun Liu, Feiyang You, Xingwei Chen, Ningxin Su#

International Conference on Machine Learning (ICML) 2026 Main (Regular)

Studies replanning overhead in long-horizon embodied agents and introduces a budgeted replanning framework with adaptive replanning decisions, token budgets, latency SLOs, and context compression.

[Project Website]

All publications