TMemAgent accepted at CIKM 2026
TMemAgent: Transferable Memory for Cross-Domain User Behaviour Simulation
Senior Lecturer
Discipline of Data Science and Artificial Intelligence School of Computing Technologies RMIT UniversityMelbourne, Australia
I received my PhD in Computer Science from the University of Melbourne in 2020 and subsequently worked there as a postdoctoral researcher, where I led the ChEMU shared-task series on information extraction from chemical patents. I joined RMIT University in 2021. I develop trustworthy, domain-specific large language model (LLM) systems for structured and high-stakes data. My work combines natural language processing, recommender systems, and knowledge-intensive AI to improve how language models learn, update, personalise, and support decision-making in scientific, biomedical, and real-world applications.
Current research is supported by competitive funding including the ARC, Wellcome and the Global Science and Technology Diplomacy Fund (GSTDF). View funded projects.
Work with me
I welcome enquiries from prospective students with strong backgrounds in deep learning, large language models, and related areas. Please email your CV, transcripts, and relevant research experience, such as publications or a minor thesis. Visiting scholars, visiting students, and industry partners are also welcome.
Current targeted opening
I am recruiting one PhD student to work on LLM hallucination mitigation and retrieval-augmented generation (RAG)-based reasoning. Enquiries on broader topics aligned with my research in trustworthy AI, natural language processing, recommender systems, and AI for science are also welcome.
Recent research, funding and group milestones.
TMemAgent: Transferable Memory for Cross-Domain User Behaviour Simulation
Position Bias Undermines Preference Consistency in Listwise LLM-Based Reranking
I Am No One: Style-Aware Paraphrasing for Text Anonymization
Artificial Intelligence (AI) in Collaborative Robotics for Advanced Manufacturing Factories
AI4You(th): Advancing Generative AI for Personalized and Collaborative Youth Mental Health Care
A collaboration involving Orygen, the University of Melbourne and RMIT University, in partnership with Google Health.
Reduce Hallucination in Large Language Models via Knowledge-Based Reasoning