Curriculum Vitae
Research Fellow (Level B), IElab, The University of Queensland
Research in information retrieval, efficient LLM systems, retrieval-augmented generation, search agents, and evidence-based medicine search, with first-author work at SIGIR, WSDM, WWW, ECIR, and EACL.
Brisbane, Australia shuai.wang2@uq.edu.au Google Scholar GitHub SkimSearchAgent
I build search and LLM systems that are effective, efficient, and inspectable for expert workflows, with a focus on deep-research agents, systematic review search, and evidence-grounded generation.
Shuai Wang and Shengyao Zhuang and Guido Zuccon. 2021. BERT-based Dense Retrievers Require Interpolation with BM25 for Effective Passage Retrieval. In The Proceedings of the 2021 ACM SIGIR on International Conference on Theory of Information Retrieval (ICTIR 2021).
Shengyao Zhuang and Hang Li and Shuai Wang and Guido Zuccon. 2021. IELAB at TREC Deep Learning Track 2021. In TREC 2021 Deep Learning Track.
Shuai Wang and Hang Li and Harry Scells and Daniel Locke and Guido Zuccon. 2021. MeSH Term Suggestion for Systematic Review Literature Search. In Australasian Document Computing Symposium (ADCS 2021).
Shuai Wang and Harry Scells and Ahmed Mourad and Guido Zuccon. 2022. SDR for Systematic Reviews: A Reproducibility Study. In Proceedings of the 44th European Conference on Information Retrieval (ECIR 2022).
Shuai Wang and Harry Scells and Justin Clark and Guido Zuccon and Bevan Koopman. 2022. From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022).
Hang Li* and Shuai Wang* and Shengyao Zhuang and Ahmed Mourad and xueguang-ma and jimmy-lin and Guido Zuccon. 2022. To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022).
Shuai Wang and Harry Scells and Bevan Koopman and Guido Zuccon. 2022. Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search. In Intelligent Systems with Applications (ISWA) Technology-Assisted Review Systems Special Issue.
Shuai Wang and Harry Scells and Bevan Koopman and Guido Zuccon. 2022. Neural Rankers for Effective Screening Prioritization in Medical Systematic Review Literature Search. In Australasian Document Computing Symposium (ADCS 2022).
Shuai Wang and Hang Li and Guido Zuccon. 2023. MeSH Suggester: A Library and System for MeSH Term Suggestion for Systematic Review Boolean Query Construction. In the 16th Web Search and Data Mining Conference WSDM 2023 (WSDM2023).
Shuai Wang, and Guido Zuccon. 2023. Balanced Topic Aware Sampling for Effective Dense Retriever: A Reproducibility Study. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023).
Shuai Wang, Harrisen Scells, Bevan Koopman and Guido Zuccon. 2023. Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search? In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023).
Shuai Wang, Harrisen Scells, Martin Potthast, Bevan Koopman and Guido Zuccon. 2023. Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation. In Proceedings of the international ACM SIGIR Conference on Information Retrieval in the Asia Pacific November 26-29, 2023 (SIGIR-AP 2023).
Shuai Wang, Harrisen Scells, Shengyao Zhuang, Martin Potthast, Bevan Koopman and Guido Zuccon. 2023. Zero-shot Generative Large Language Models for Systematic Review Screening Automation. In Proceedings of the 46th European Conference on Information Retrieval (ECIR 2024).
Shuai Wang, Shengyao Zhuang, Bevan Koopman and Guido Zuccon. 2024. ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search. (Accepted in WWW2025).
Shuai Wang, Ekaterina Khramtsova, Shengyao Zhuang and Guido Zuccon. 2024. FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024).
Lukas Gienapp, Harrisen Scells, Niklas Deckers, Janek Bevendorff, Shuai Wang, Johannes Kiesel, Shahbaz Syed, Maik Fröbe, Guido Zuccon, Benno Stein, Matthias Hagen and Martin Potthast. 2024. Evaluating Generative Ad Hoc Information Retrieval. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024).
Shuai Wang, Shengyao Zhuang and Guido Zuccon. 2024. Large Language Models for Stemming: Promises, Pitfalls and Failures. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024).
Shuoqi Sun, Shengyao Zhuang, Shuai Wang and Guido Zuccon. 2024. Zero-shot Generative Large Language Models for Systematic Review Screening Automation. (Accepted in ECIR 2025).
David Rau, Hervé Déjean, Nadezhda Chirkova, Thibault Formal, Shuai Wang, Vassilina Nikoulina and Stéphane Clinchant. 2024. BERGEN: A Benchmarking Library for Retrieval-Augmented Generation. (Accepted in EMNLP2024 Findings).
David Rau*, Shuai Wang*, Hervé Déjean and Stéphane Clinchant. 2024. Context Embeddings for Efficient Answer Generation in RAG. (Accepted in WSDM2025).
Shengyao Zhuang*, Shuai Wang*, Bevan Koopman and Guido Zuccon. 2024. Starbucks: Improved Training for 2D Matryoshka Embeddings. (Accepted in ECIR2026).
Maik Fröbe, Andrew Parry, Harrisen Scells, Shuai Wang, Shengyao Zhuang, Guido Zuccon, Martin Potthast and Matthias Hagen. 2025. Corpus Subsampling: Estimating the Effectiveness of Neural Retrieval Models on Large Corpora. In: Hauff, C., et al. Advances in Information Retrieval. ECIR 2025. Lecture Notes in Computer Science, vol 15572. Springer, Cham. https://doi.org/10.1007/978-3-031-88708-6_29.
Shuai Wang, Shengyao Zhuang, Bevan Koopman and Guido Zuccon. 2025. 2D Matryoshka Training for Information Retrieval. (Accepted SIGIR-2025).
Shuai Wang, Harrisen Scells, Bevan Koopman and Guido Zuccon. 2025. Reassessing Large Language Model Boolean Query Generation for Systematic Reviews. (Accepted SIGIR-2025).
Zheng Yao, Shuai Wang and Guido Zuccon. 2025. Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition (Accepted SIGIR-2025).
Bevan Koopman, Hang Li, Shuai Wang and Guido Zuccon. 2025. Evalugator🐊—Rapid, Agile Development and Evaluation of Retrieval Augmented Generation Systems Without Labels (Accepted ECIR-2026).
Shuai Wang, Harrisen Scells, Bevan Koopman and Guido Zuccon. 2025. AutoBool: Reinforcement-Learned LLM for Effective Automatic Systematic Reviews Boolean Query Generation (Accepted EACL-2026).
Yongjie Zhou*, Shuai Wang*, Bevan Koopman and Guido Zuccon. 2026. Beyond Chunk-Then-Embed: A Comprehensive Taxonomy and Evaluation of Document Segmentation Strategies for Information Retrieval. (Accepted SIGIR-2026).
Shengyao Zhuang, Xueguang Ma, Zheng Yao, Shuai Wang, Bevan Koopman, Jimmy Lin and Guido Zuccon. 2026. Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning. (Accepted SIGIR-2026).
Yu Yin, Shuai Wang, Bevan Koopman and Guido Zuccon. 2026. The Vulnerability of LLM Rankers to Prompt Injection Attacks. (Accepted SIGIR-2026).
Shuai Wang, Yu Yin, Shengyao Zhuang, Bevan Koopman and Guido Zuccon. 2026. DiffRetriever: Parallel Representative Tokens for Retrieval with Diffusion Language Models. arXiv preprint arXiv:2605.07210.
Yu Yin, Shuai Wang, Bevan Koopman and Guido Zuccon. 2026. Can It Reach the Generator? Investigating the Survival of Prompt-Injection Attacks in Realistic RAG Settings. arXiv preprint arXiv:2605.28017.
Shuai Wang, Haodong Chen, Yu Yin, Shengyao Zhuang, Bevan Koopman and Guido Zuccon. 2026. Search, Inspect, Fetch: Exploiting Structure-Aware Boolean Retrieval for Deep-Research Agents. arXiv preprint arXiv:2608.02751.
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