Sukai Huang

黄苏恺 | Sukai Huang

Postdoctoral Research Fellow, Monash University

📚 7 Publications 📝 3 First-Author Top-Tier 🔗 57 Citations 📊 h-index 4

About Me

I am a Postdoctoral Research Fellow at Monash University, where I serve as a core member on the DARPA HARNESS and ONR SEA-AI projects, leading independent research streams on neuro-symbolic planning and maritime autonomy. I work with A/Prof. Hamid Rezatofighi (Lead PI, DARPA HARNESS; Area Chair, CVPR/NeurIPS/IROS) in the Vision & Learning for Autonomous AI (VL4AI) Group, with a focus on neuro-symbolic AI, large language models for embodied agents, and robot planning & reasoning.

I completed my PhD at the University of Melbourne (2021–2025), supervised by A/Prof. Nir Lipovetzky (ICAPS Executive Council Mentoring Chair, 2025–2031) and Prof. Trevor Cohn (Research Scientist, Google Research Australia), where my thesis investigated Integrating Natural Language for Sequential Decision Problems. Prior to that, I earned a Bachelor of Advanced Computing (Honours) from the Australian National University, supervised by Prof. Jochen Renz (ARC Future Fellow; Former Head of ANU AI Group).

Research Vision: I believe the next wave of AI will be agentic and verifiable systems that plan, act, and explain their decisions by composing neural perception with symbolic representations and logic. My work bridges the gap between probabilistic language models and formal reasoning, aiming to build embodied agents that are safe-by-default, auditable, and robust.

🔥 News

  • Jan 2026 — Paper accepted at AAMAS 2026 (First Author): Neurosymbolic Active Goal Recognition in Partially Observable Environments.
  • Jan 2026 — Paper accepted at ACL 2026 (Corresponding Author): ALIGN: Word Association Learning for Cultural Alignment in LLMs.
  • Jan 2026 — Paper accepted at ICLR 2026: MATA: A Trainable Hierarchical Automaton System for Multi-Agent Visual Reasoning.
  • Jun 2025 — Started Postdoc at Monash University; joined DARPA HARNESS and ONR SEA-AI projects as a core member.
  • Jun 2025 — Invited to give a presentation at the Kingston AI Symposium 2026 in Adelaide, Australia.
  • Jun 2025 — ICAPS 2025 Oral Presentation: Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation.
  • Mar 2025 — Two oral presentations at the LM4Plan Workshop @ AAAI 2025.

Selected Publications

Sorted by contribution significance. First-author / corresponding-author papers are highlighted.

  1. 【First Author】 Sukai Huang, Chenyuan Zhang, Hamid Rezatofighi, Mor Vered, and Buser Say. “Neurosymbolic Active Goal Recognition in Partially Observable Environments.” International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2026.
  2. 【First Author】 Sukai Huang, Nir Lipovetzky, and Trevor Cohn. “Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation.” International Conference on Automated Planning and Scheduling (ICAPS), 2025. 🏷️ Oral Presentation
  3. 【First Author】 Sukai Huang, Nir Lipovetzky, and Trevor Cohn. “Planning in the Dark: LLM-Symbolic Planning Pipeline without Experts.” AAAI Conference on Artificial Intelligence (AAAI), 2025.
  4. 【Corresponding Author】 Chunhua Liu, Kabir Manandhar Shrestha, and Sukai Huang. “ALIGN: Word Association Learning for Cultural Alignment in Large Language Models.” Annual Meeting of the Association for Computational Linguistics (ACL), 2026.
  5. Zhixi Cai, Fucai Ke, Kevin Leo, Sukai Huang, Maria Garcia de la Banda, Peter J. Stuckey, and Hamid Rezatofighi. “MATA: A Trainable Hierarchical Automaton System for Multi-Agent Visual Reasoning.” International Conference on Learning Representations (ICLR), 2026.
  6. Simon De Deyne, Sukai Huang, Kabir Manandhar Shrestha, Lea Frermann, and Chunhua Liu. “Fine-tuned Large Language Models Predict Human Word Associations and Improve Performance across Lexical and Semantic Processing Tasks.” Annual Meeting of the Cognitive Science Society (CogSci), 2026.
  7. 【First Author】 Sukai Huang, Shu-Wei Liu, Nir Lipovetzky, and Trevor Cohn. “The Dark Side of Rich Rewards: Understanding and Mitigating Noise in VLM Rewards.” ICAPS 2025 Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL). [arXiv]

Pre-prints & Code: [Google Scholar] | [GitHub]

Research Projects

DARPA HARNESS Project

Hierarchical Abstractions and Reasoning for Neuro-Symbolic Systems

  • Role: Research Fellow
  • Period: 2025 – Present
  • Focus: Developing neuro-symbolic planning and granularity-aware instruction-following modules for autonomous robotic systems, integrating perception, reasoning, and planning through neuro-symbolic frameworks.

ONR SEA-AI Project

Neuro-Symbolic Enhanced Autonomy for Maritime Scene Understanding

  • Role: Research Fellow
  • Period: 2025 – Present
  • Focus: Leading mission analysis from natural language, world-model construction, active perception, and sequential decision-making components for maritime autonomous systems.

Academic Services

  • Reviewer: ICAPS, ACL Rolling Review (ARR), and the Artificial Intelligence journal.
  • Workshop Organizer / PC Member: Active contributor to the neuro-symbolic AI and LLM-planning communities.

Awards & Honors

  • AAAI 2025 Scholarship, Association for the Advancement of Artificial Intelligence.
  • ICAPS 2025 Scholarship, International Conference on Automated Planning and Scheduling.
  • Kingston AI Symposium 2026 — Invited Presenter, Adelaide, Australia.
  • Winner, Aesthetic Track of the Angry Birds Level Generation Competition, IEEE COG 2020.
  • ANU Summer Scholarship Program 2019 — Researcher on emotion recognition models.
  • ANU Chancellor’s Letters of Commendation (2018, 2020).
  • ANU Dean’s Award (2019).

Teaching & Industry Experience

  • Tutor (Feb 2020 – Jul 2021), Australian National University
    Planned and delivered interactive tutorials on Python Programming and Data Management for undergraduate students.
  • Machine Learning Engineer (May 2022 – Nov 2023), FINVISE London (Remote Contract)
    Built an Automated Valuation Model (AVM) achieving >80% accuracy; designed a multimodal data pipeline handling 20M+ property records; proposed Masked Autoencoder (MAE) solutions for missing-data challenges.

Education

  • PhD in Engineering & IT — University of Melbourne, Australia, 2021–2025
    Thesis: Integrating Natural Language for Sequential Decision Problems
    Scholarship: Melbourne Research Scholarship
    Supervisors: A/Prof. Nir Lipovetzky (ICAPS Executive Council Mentoring Chair, 2025–2031) & Prof. Trevor Cohn (Research Scientist, Google Research Australia)
  • Bachelor of Advanced Computing (Honours) — Australian National University, Australia, 2017–2021
    Honours: First Class Honours | GPA: 6.84 / 7.00
    Specialization: Machine Learning Systems
    Supervisor: Prof. Jochen Renz (ARC Future Fellow; Former Head of ANU AI Group)

Contact

I am open to academic collaborations and faculty opportunities. Please feel free to reach out!

Full CV

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