Research Experience

• 04/2025 – Present Postdoc
  UNSW Sydney
  • Project: Harnessing AI to reduce loneliness.
  • Advisor: Prof. Michael Thielscher.
  • Research areas: language models, knowledge representation and reasoning, epistemic reasoning.
• 06/2021 – 03/2025 PhD in Artificial Intelligence
  The University of Melbourne
  • Thesis: Developing Explainable AI-Assisted Decision-Making Systems.
  • Advisors: Prof. Tim Miller (primary), Prof. Liz Sonenberg, Dr. Ronal Singh.
  • Research areas: Explainable AI, human-AI interaction, computer vision, decision support systems.

Teaching Experience

• 06/2026 – Present Supervisor
  UNSW Sydney, School of Computer Science and Engineering
  • Propose and supervise projects on explainable AI and language models for undergraduate and master's students.
  • Supervised 12+ students (group-based and individual projects).
• S1/S2 2023/2024 Academic Tutor (Teaching Assistant)
  The University of Melbourne
  • Course: AI Planning for Autonomy, COMP90054 (Graduate level).
  • Topics covered: Search algorithms and heuristic functions, classical (AI) planning, Markov Decision Processes, MCTS, reinforcement learning, game theory.

Academic Service

• PC Member / Reviewer: EMNLP 2026, FAccT 2025/6/7, HCOMP 2023.
• Journal Reviewing: ACM TOIT, Behavior Research Methods.

Selected Awards/Achievements

• Google Conference Grant (2023).
• CIS Nominee for Apple Scholars in AI/ML PhD Fellowships (2022/2023).
• Melbourne Centre for Data Science (MCDS) Top Up Scholarship (2022).
• Melbourne Research Scholarship: Full fee offset and stipend.
• Second Prize - Electronic Trading Competition - Jane Street Capital (2019).

Activities

• Presenter at Australasian Joint Conference on Artificial Intelligence (AJCAI) Doctoral Consortium - Perth, Australia - 12/2022.
• Participant at FAccT, Innovative Methods for Critical Studies of Emerging Technologies Workshop - Australian National University, Canberra - 09/2022.
• Final round participant at Melbourne Facebook Hackathon, Australia - 05/2019.

Skills

• Programming: Python, Bash.
• Machine learning, statistics, deep learning, reinforcement learning, high-performance computing (HPC).
• Web development: HTML, CSS, Javascript, nginx, web frameworks and libraries (e.g., FastAPI, React, Gradio).
• Designing and conducting human experiments, qualitative/quantitative data analysis.