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Dr Shuai Wang
Dr

Shuai Wang

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Overview

Background

Shuai Wang is a Postdoctoral Research Fellow at ielab (led by Professor Guido Zuccon) at The University of Queensland. His research focuses on information retrieval (IR), large language models (LLMs), and natural language processing (NLP), with particular emphasis on developing AI-driven systems for knowledge-intensive tasks.

Shuai completed his PhD on automating medical systematic reviews using neural retrieval systems and generative models (thesis: AI-driven Automated Systematic Reviews). His doctoral work encompassed automatic MeSH term suggestion, screening prioritization, seed-driven retrieval methods, and automatic Boolean query formulation.

His broader research contributions span federated search optimization and improving model efficiency in IR and retrieval-augmented generation (RAG) applications. His work has been published at premier venues including SIGIR, ECIR, WSDM, and EMNLP. He has served on program committees for SIGIR, ECIR, ICTIR, and TOIS, and as SIGIR-AP Queensland local satellite chair.

Education

  • PhD, The University of Queensland (2021–2025)
  • Master's degree, The University of Queensland (2020–2021)
  • Bachelor's degree, The University of Western Australia (2017–2019)

Availability

Dr Shuai Wang is:
Available for supervision

Qualifications

  • Masters (Coursework) of Software Engineering, The University of Queensland
  • Doctor of Philosophy of Information Retrieval and Web Search, The University of Queensland

Works

Search Professor Shuai Wang’s works on UQ eSpace

24 works between 2021 and 2025

21 - 24 of 24 works

2022

Conference Publication

To Interpolate or not to Interpolate: PRF, dense and sparse retrievers

Li, Hang, Wang, Shuai, Zhuang, Shengyao, Mourad, Ahmed, Ma, Xueguang, Lin, Jimmy and Zuccon, Guido (2022). To Interpolate or not to Interpolate: PRF, dense and sparse retrievers. SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11-15 July 2022. New York, United States: Association for Computing Machinery. doi: 10.1145/3477495.3531884

To Interpolate or not to Interpolate: PRF, dense and sparse retrievers

2022

Conference Publication

Seed-driven document ranking for systematic reviews: a reproducibility study

Wang, Shuai, Scells, Harrisen, Mourad, Ahmed and Zuccon, Guido (2022). Seed-driven document ranking for systematic reviews: a reproducibility study. 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, 10–14 April 2022. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-99736-6_46

Seed-driven document ranking for systematic reviews: a reproducibility study

2021

Conference Publication

MeSH Term Suggestion for Systematic Review Literature Search

Wang, Shuai, Li, Hang, Scells, Harrisen, Locke, Daniel and Zuccon, Guido (2021). MeSH Term Suggestion for Systematic Review Literature Search. Australasian Document Computing Symposium, Melbourne, VIC, Australia, 9 December 2021. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3503516.3503530

MeSH Term Suggestion for Systematic Review Literature Search

2021

Conference Publication

BERT-based Dense Retrievers Require Interpolation with BM25 for Effective Passage Retrieval

Wang, Shuai, Zhuang, Shengyao and Zuccon, Guido (2021). BERT-based Dense Retrievers Require Interpolation with BM25 for Effective Passage Retrieval. The ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR), Canada, 11 July 2021. New York, NY, USA: Association for Computing Machinery, Inc. doi: 10.1145/3471158.3472233

BERT-based Dense Retrievers Require Interpolation with BM25 for Effective Passage Retrieval

Funding

Past funding

  • 2023 - 2024
    From Search to Synthesis: Automating the Systematic Review Creation Process
    Universities Australia - Germany Joint Research Co-operation Scheme
    Open grant

Supervision

Availability

Dr Shuai Wang is:
Available for supervision

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Media

Enquiries

For media enquiries about Dr Shuai Wang's areas of expertise, story ideas and help finding experts, contact our Media team:

communications@uq.edu.au