Research Experience

• Postdoc - UNSW Sydney - 04/2025 - present
  • Project: Harnessing AI to reduce loneliness
  • Advisor: Prof. Michael Thielscher
  • Research areas: language models, knowledge representation and reasoning, epistemic reasoning.


• PhD in Artificial Intelligence - The University of Melbourne - 06/2021 - 03/2025
  • 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

• Supervisor - UNSW Sydney, School of Computer Science and Engineering - 06/2026 - Present
  • Propose and supervise projects on explainable AI and language models for undergraduate and master's students.
  • Supervised 12+ students


• Academic Tutor (Teaching Assistant) - The University of Melbourne - S1/S2 2023/2024
  • 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, 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, PyTorch, Scikit-learn, Pandas, Bash
• Machine learning, statistics, deep learning and reinforcement learning, high-performance computing (HPC)
• Web development: HTML, CSS, Javascript, nginx, web frameworks and libraries (e.g., FastAPI, React, Gradio)
• Version control: Git
• Designing and conducting human experiments, qualitative/quantitative data analysis