Overview
Background
Shuai Wang is a Research Fellow at ielab, The University of Queensland, working on AI-powered search. He builds systems that find information and answer questions, using large language models and retrieval-augmented generation (RAG), and he focuses on making those systems faster and cheaper to run. 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. A lot of his work comes back to one practical question: how do we get good search and reliable answers without depending on expensive, closed commercial AI?
Shuai has published 25+ papers at venues such as SIGIR, WSDM, ECIR, and EMNLP. He coordinates and teaches INFS7410 (Information Retrieval and Web Search) at UQ.
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.
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
Research interests
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Efficient, Effective and Adaptive Retrieval
Search systems should return the right results without wasting computation. This area studies dense and sparse retrieval that stays accurate while controlling cost, and adaptive models that scale their effort to the difficulty of each query. Topics include multi-representation embeddings, model efficiency, and the trade-offs between effectiveness, speed, and memory in large-scale search.
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Efficient Retrieval-Augmented Generation (RAG)
RAG lets language models answer questions using retrieved evidence, but it is costly to run at scale. This area focuses on making RAG cheaper and faster through context compression, reduced redundant computation, and better memory use, so that reliable, grounded question answering can run on local hardware rather than depending only on large commercial APIs.
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Effective Search in the Agent Era
Search looks very different when the user is an AI agent rather than a person, so the way retrieval works needs rethinking. Agents issue many queries, chain steps together, and act on what they find, which calls for search designed specifically for them. This area asks how to build retrieval that is effective, efficient, and reliable for agentic systems.
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AI for Systematic Reviews and Clinical Evidence
Finding and screening medical evidence is slow and labour-intensive. This area applies retrieval and language models to evidence synthesis, including query formulation, term suggestion, and screening automation, helping clinicians and researchers work faster while keeping the high recall and transparency that high-stakes reviews demand.
Works
Search Professor Shuai Wang’s works on UQ eSpace
2026
Book Chapter
A simple sketch of a disposable coffee cup with a lid. The cup features a green circle in the center, possibly representing a logo or design element. The drawing is outlined in black with minimal detail. Starbucks: Improved Training for 2D Matryoshka Embeddings
Zhuang, Shengyao, Wang, Shuai, Zheng, Fabio, Koopman, Bevan and Zuccon, Guido (2026). A simple sketch of a disposable coffee cup with a lid. The cup features a green circle in the center, possibly representing a logo or design element. The drawing is outlined in black with minimal detail. Starbucks: Improved Training for 2D Matryoshka Embeddings. Lecture Notes in Computer Science. (pp. 67-82) Cham: Springer Nature Switzerland. doi: 10.1007/978-3-032-21289-4_5
2026
Conference Publication
AutoBool: Reinforcement-Learned LLM for Effective Automatic Systematic Reviews Boolean Query Generation
Wang, Shuai, Scells, Harrisen, Koopman, Bevan and Zuccon, Guido (2026). AutoBool: Reinforcement-Learned LLM for Effective Automatic Systematic Reviews Boolean Query Generation. Stroudsburg, PA, USA: Association for Computational Linguistics. doi: 10.18653/v1/2026.eacl-long.68
2026
Conference Publication
EvalugatorA stylized green crocodile with a yellow grid pattern on its back, resembling a calculator. The design combines elements of a crocodile and a calculator, symbolizing a playful or creative concept.—Rapid, Agile Development and Evaluation of Retrieval Augmented Generation Systems Without Labels
Koopman, Bevan, Li, Hang, Wang, Shuai and Zuccon, Guido (2026). EvalugatorA stylized green crocodile with a yellow grid pattern on its back, resembling a calculator. The design combines elements of a crocodile and a calculator, symbolizing a playful or creative concept.—Rapid, Agile Development and Evaluation of Retrieval Augmented Generation Systems Without Labels. Springer Science and Business Media Deutschland GmbH. doi: 10.1007/978-3-032-21321-1_8
2025
Conference Publication
2D Matryoshka Training for Information Retrieval
Wang, Shuai, Zhuang, Shengyao, Koopman, Bevan and Zuccon, Guido (2025). 2D Matryoshka Training for Information Retrieval. 48th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR 2025), Padua, Italy, 13-18 July 2025. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3726302.3730330
2025
Conference Publication
Reassessing Large Language Model Boolean query generation for systematic reviews
Wang, Shuai, Scells, Harrisen, Koopman, Bevan and Zuccon, Guido (2025). Reassessing Large Language Model Boolean query generation for systematic reviews. 48th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR 2025), Padua, Italy, 13-18 July 2025. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3726302.3730329
2025
Conference Publication
Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition
Yao, Zheng, Wang, Shuai and Zuccon, Guido (2025). Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition. 48th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR 2025), Padua, Italy, 13-18 July 2025. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3726302.3730332
2025
Conference Publication
ReSLLM: large language models are strong resource selectors for federated search
Wang, Shuai, Zhuang, Shengyao, Koopman, Bevan and Zuccon, Guido (2025). ReSLLM: large language models are strong resource selectors for federated search. The ACM Web Conference 2025, Sydney, NSW Australia, 28 April-2 May 2025. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3701716.3715595
2025
Conference Publication
An investigation of prompt variations for zero-shot LLM-based rankers
Sun, Shuoqi, Zhuang, Shengyao, Wang, Shuai and Zuccon, Guido (2025). An investigation of prompt variations for zero-shot LLM-based rankers. 47th European Conference on Information Retrieval, Lucca, Italy, 6-10 April 2025. Cham, Switzerland: Springer Cham. doi: 10.1007/978-3-031-88711-6_12
2025
Conference Publication
Corpus subsampling: estimating the effectiveness of neural retrieval models on large corpora
Fröbe, Maik, Parry, Andrew, Scells, Harrisen, Wang, Shuai, Zhuang, Shengyao, Zuccon, Guido, Potthast, Martin and Hagen, Matthias (2025). Corpus subsampling: estimating the effectiveness of neural retrieval models on large corpora. 47th European Conference on Information Retrieval, Lucca, Italy, 6-10 April 2025. Cham, Switzerland: Springer Cham. doi: 10.1007/978-3-031-88708-6_29
2025
Conference Publication
Context embeddings for efficient answer generation in retrieval-augmented generation
Rau, David, Wang, Shuai, Déjean, Hervé, Clinchant, Stéphane and Kamps, Jaap (2025). Context embeddings for efficient answer generation in retrieval-augmented generation. 18th International Conference on Web Search and Data Mining-WSDM, Hannover, Germany, 10-14 March 2025. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3701551.3703527
2024
Conference Publication
Large language models based stemming for information retrieval: promises, pitfalls and failures
Wang, Shuai, Zhuang, Shengyao and Zuccon, Guido (2024). Large language models based stemming for information retrieval: promises, pitfalls and failures. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657949
2024
Conference Publication
FeB4RAG: evaluating federated search in the context of retrieval augmented generation
Wang, Shuai, Khramtsova, Ekaterina, Zhuang, Shengyao and Zuccon, Guido (2024). FeB4RAG: evaluating federated search in the context of retrieval augmented generation. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657853
2024
Conference Publication
Evaluating generative ad hoc information retrieval
Gienapp, Lukas, Scells, Harrisen, Deckers, Niklas, Bevendorff, Janek, Wang, Shuai, Kiesel, Johannes, Syed, Shahbaz, Fröbe, Maik, Zuccon, Guido, Stein, Benno, Hagen, Matthias and Potthast, Martin (2024). Evaluating generative ad hoc information retrieval. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieva, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657849
2024
Journal Article
Report on the Collab-a-Thon at ECIR 2024
MacAvaney, Sean, Roegiest, Adam, Lipani, Aldo, Parry, Andrew, Engelmann, Björn Engelmann, Kreutz, Christin Katharina, Meng, Chuan, Frayling, Erlend, Yang, Eugene, Schlatt, Ferdinand, Faggioli, Guglielmo, Scells, Harrisen, Atanassova, Iana, Friese, Jana, Bevendorff, Janek, Sanz-Cruzado, Javier, Trippas, Johanne, Pathak, Kanaad, Dhole, Kaustubh, Azzopardi, Leif, Fröbe, Maik, Bertin, Marc, Prasad, Nishchal, Zerhoudi, Saber, Wang, Shuai, Chatterjee, Shubham, Jaenich, Thomas, Kruschwitz, Udo, Wang, Xi and Long, Zijun (2024). Report on the Collab-a-Thon at ECIR 2024. ACM SIGIR Forum, 58 (1), 1-11. doi: 10.1145/3687273.3687287
2024
Conference Publication
Zero-shot generative large language models for systematic review screening automation
Wang, Shuai, Scells, Harrisen, Zhuang, Shengyao, Potthast, Martin, Koopman, Bevan and Zuccon, Guido (2024). Zero-shot generative large language models for systematic review screening automation. 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, United Kingdom, 24-28 March 2024. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-56027-9_25
2024
Conference Publication
BERGEN: a benchmarking library for retrieval-augmented generation
Rau, David, Déjean, Hervé, Chirkova, Nadezhda, Formal, Thibault, Wang, Shuai, Clinchant, Stéphane and Nikoulina, Vassilina (2024). BERGEN: a benchmarking library for retrieval-augmented generation. 2024 Conference on Empirical Methods in Natural Language Processing, Miami, FL, United States, 12 - 16 November 2024. Stroudsburg, PA, United States: Association for Computational Linguistics. doi: 10.18653/v1/2024.findings-emnlp.449
2023
Conference Publication
Generating natural language queries for more effective systematic review screening prioritisation
Wang, Shuai, Scells, Harrisen, Koopman, Bevan, Potthast, Martin and Zuccon, Guido (2023). Generating natural language queries for more effective systematic review screening prioritisation. SIGIR-AP 2023 - Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, Beijing, China, 26 - 28 November 2023. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3624918.3625322
2023
Conference Publication
Can ChatGPT write a good Boolean query for systematic review literature search?
Wang, Shuai, Scells, Harrisen, Koopman, Bevan and Zuccon, Guido (2023). Can ChatGPT write a good Boolean query for systematic review literature search?. 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan, 23 - 27 July 2023. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3539618.3591703
2023
Conference Publication
Balanced topic aware sampling for effective dense retriever: a reproducibility study
Wang, Shuai and Zuccon, Guido (2023). Balanced topic aware sampling for effective dense retriever: a reproducibility study. 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan, 23 - 27 July 2023. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3539618.3591915
2023
Conference Publication
MeSH suggester: a library and system for MeSH term suggestion for systematic review Boolean query construction
Wang, Shuai, Li, Hang and Zuccon, Guido (2023). MeSH suggester: a library and system for MeSH term suggestion for systematic review Boolean query construction. Sixteenth ACM International Conference on Web Search and Data Mining, Singapore, Singapore, 27 February - 3 March 2023. New York, NY, United States: ACM. doi: 10.1145/3539597.3573025
Funding
Past funding
Supervision
Availability
- Dr Shuai Wang is:
- Available for supervision
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