Overview
Background
Hang is a Postdoctoral Research Fellow in IELab within the School of Electrical Engineering and Computer Science at the University of Queensland, Australia, where he works closely with Prof. Guido Zuccon, A/Prof. Bevan Koopman, and Dr. Ahmed Mourad. Prior to Ph.D, he received his Bachelor of Science degree in Computer Science at the University of Minnesota Twin-Cities, United States, 2016.
Hang's research interests span Information Retrieval (IR), Large Language Models (LLMs), Natural Language Processing (NLP), Agentic Search, Retrieval-Augmented Generation (RAG), and Machine Learning (ML). In particular, he is interested in developing intelligent search and information-access systems that combine effective retrieval with the reasoning and generation capabilities of LLMs. His research explores topics including neural and dense retrieval, retrieval enhancement and feedback, LLM-based relevance assessment, agentic and conversational search, RAG system design and evaluation, and the efficient and reliable integration of LLMs into real-world information retrieval pipelines.
Hang publishes at premier academic venues in IR (e.g. SIGIR, ECIR, WSDM, WWW, TOIS, IJDL). His work was supported by Grains Research and Development Corporation, through the AgAsk project during Ph.D.
Availability
- Dr Hang Li is:
- Available for supervision
Fields of research
Qualifications
- Bachelor of Computer Science, University of Minnesota-Twin Cities
- Doctor of Philosophy of Computer Science, The University of Queensland
Research interests
-
Information Retrieval and Search Systems
My research focuses on Information Retrieval (IR), including neural retrieval, dense and sparse retrieval, ranking and reranking, query representation, and relevance modelling. I am particularly interested in developing effective and efficient search systems that improve how users access and interact with large-scale information collections.
-
Large Language Models for Information Retrieval
I investigate the integration of Large Language Models (LLMs) into information retrieval systems, including LLM-based retrieval, reranking, query representation, relevance assessment, and search enhancement. My research explores how the reasoning and language understanding capabilities of LLMs can improve retrieval effectiveness while addressing challenges in efficiency, reliability, and evaluation.
-
Agentic Search and AI Agents
My research explores Agentic Search and LLM-based autonomous agents that can plan, search, reason over retrieved information, and iteratively refine their information-seeking strategies. I am interested in agentic information retrieval, deep research systems, tool-using agents, multi-step search, and the evaluation of agent behaviour and search trajectories.
-
Retrieval-Augmented Generation
I study Retrieval-Augmented Generation (RAG) systems that combine information retrieval with generative language models to produce grounded and contextually relevant responses. My work covers retrieval pipeline design, document and passage retrieval, generation, grounding, system evaluation, and the development of reliable RAG systems for real-world applications, including scientific and clinical information access.
-
Neural and Dense Information Retrieval
I develop neural retrieval methods based on learned representations and language models, with particular interests in dense retrieval, late interaction, query and document representation, pseudo-relevance feedback, and retrieval enhancement. My research investigates how richer representations and feedback signals can improve retrieval effectiveness while maintaining practical efficiency.
-
Evaluation of Search and Generative AI Systems
I investigate methods for evaluating modern information retrieval, RAG, and generative AI systems. This includes LLM-based relevance assessment, LLM-as-a-Judge methods, retrieval evaluation without extensive human labels, evaluation reliability and bias, and scalable approaches for assessing complex search and question-answering systems.
-
Natural Language Processing and Machine Learning
My broader research interests include Natural Language Processing (NLP) and Machine Learning (ML), particularly their application to information access, language understanding, question answering, and generative AI. I am interested in developing and applying machine learning methods that enable systems to retrieve, reason over, and generate information more effectively.
Works
Search Professor Hang Li’s works on UQ eSpace
2020
Conference Publication
IELAB for TREC Conversational Assistance Track (CAsT) 2020
Sebastian, Cross, Li, Hang, Zhuang, Arvin, Ahmed, Mourad, Bevan, Koopman and Guido, Zuccon (2020). IELAB for TREC Conversational Assistance Track (CAsT) 2020. 29th Text REtrieval Conference, TREC 2020, Online, 16-20 November 2020. Gaithersburg, MD United States: National Institute of Standards and Technology (NIST).
Supervision
Availability
- Dr Hang Li is:
- Available for supervision
Looking for a supervisor? Read our advice on how to choose a supervisor.
Supervision history
Current supervision
-
Doctor Philosophy
Evaluation of Agentic Information Retrieval Systems via Simulation for Next-Generation Search
Associate Advisor
Other advisors: Professor Guido Zuccon
Media
Enquiries
For media enquiries about Dr Hang Li's areas of expertise, story ideas and help finding experts, contact our Media team: