Dr Hang Li
Dr

Hang Li

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Background

Hang is a Postdoctoral Research Officer 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 works at the intersection of Information Retrieval (IR), Large Language Models (LLMs), Natural Language Processing (NLP), and Machine Learning (ML) applications, where he utilises different relevance feedbacks to empower the information retrieval system. His recent works seek to address the gap between relevance feedback, deep language models, and information retrieval through different approaches that helps to improve the IR system effectiveness with minimal efficiency cost.

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

Artificial intelligence Context learning Deep learning Empirical software engineering Information and Computing Sciences Knowledge representation and reasoning Machine learning Natural language processing Neural networks Programming languages Reinforcement learning Software engineering

Qualifications

  • Bachelor of Computer Science, University of Minnesota-Twin Cities
  • Doctor of Philosophy of Computer Science, The University of Queensland

Search Professor Hang Li’s works on UQ eSpace

21 works between 2020 and 2026
All (21) Journal Article (3) Other Outputs (5) Conference Publication (13)

21 - 21 of 21 works

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).

IELAB for TREC Conversational Assistance Track (CAsT) 2020

Availability

Dr Hang Li is:
Available for supervision

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Enquiries

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