Dr. Shuai Wang is a Research Fellow at the IELab, University of Queensland, Australia. He works on Information Retrieval (IR), search agents, biomedical evidence search, and efficient RAG systems. His papers appear at SIGIR, WSDM, WWW, ECIR, EMNLP, and EACL, with 1389+ citations on Google Scholar. He completed his PhD at the University of Queensland in 2025 under the supervision of Professor Guido Zuccon, Associate Professor Bevan Koopman, and Dr. Harrisen Scells, and now serves as Course Coordinator and Lecturer for INFS7410 (Information Retrieval and Web Search) at UQ.
🎓 Academic Background
- Doctor of Philosophy - University of Queensland (2025) Read My Thesis
- Master of Engineering Science - University of Queensland (2021)
- Bachelor of Science - University of Western Australia (2019)
🔬 Research Focus
My current work is organised around a few concrete themes:
- Search agents and deep-research workflows: task decomposition, Boolean retrieval, structured inspect cards, section-level fetching, and tool-using agents
- Biomedical evidence search and systematic reviews: MeSH suggestion, screening prioritisation, Boolean query generation, seed-driven retrieval, and clinical evidence workflows
- Efficient RAG and LLM systems: context embeddings, retrieval-generation interfaces, KV-cache / memory optimisation, and more efficient inference pipelines
- Adaptive retrieval and ranking: 2D Matryoshka retrieval, routing, prompt variation studies, and more robust LLM-based rankers
- Evaluation and infrastructure: benchmarks, tooling, and reproducible evaluation for RAG, federated search, and agent workflows
👨🏫 Teaching & Mentoring
I serve as Course Coordinator and Lecturer for INFS7410 (Information Retrieval and Web Search) at UQ, teaching 120+ Master’s students across classical IR, dense retrieval, LLMs for search, RAG, and evaluation. Previously, I tutored INFS7410, INFS7205, and DATA7901/7902/7903.
I mentor students on retrieval, RAG, and biomedical NLP projects; several have progressed to PhD scholarships and SIGIR/ECIR papers. If you are interested in research collaboration, please reach out.
🌍 Industry Experience
Research Intern at Naver Labs Europe (Feb-July 2024). I co-led COCOM from method design and implementation to experiments and WSDM 2025 publication, reducing RAG inference cost by 5.69x at near-baseline answer quality.
💼 Job Opportunities
Since February 2025, I have been working at UQ — currently as a Research Fellow. I am open to academic and industry roles where search agents, retrieval systems, efficient RAG, trustworthy evaluation, or biomedical evidence technologies are central. If this matches your team, please feel free to reach out.
📰 Latest News
Three Papers Accepted in SIGIR 2026
[Reproducibility Paper]: Beyond Chunk-Then-Embed: A Comprehensive Taxonomy and Evaluation of Document Segmentation Strategies for Information Retrieval; [Reproducibility Paper]: The Vulnerability of LLM Rankers to Prompt Injection Attacks; [Short Paper]: Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning.
Serving as Communication Chair for SIGIR 2026
I will be serving as the Communication (Social Media) Chair for SIGIR 2026 conference to be held in Melbourne | Naarm, Australia.
Paper accepted in EACL2026 Main Conference
[Full Paper]: AutoBool: Reinforcement-Learned LLM for Effective Automatic Systematic Reviews Boolean Query Generation.
🏆 Selected Awards
- 2021 Best Student Paper Award, ADCS 2021 Best Student Paper Award at ADCS 2021 for "MeSH Term Suggestion for Systematic Review Literature Search".
- 2021 HUAWEI DIGIX Global AI Challenge, Champion (US$30,000) Champion of the 2021 HUAWEI DIGIX Global AI Challenge (Track 4). Team prize US$30,000.
🤝 Professional Services
I previously served as Publicity Chair for SIGIR 2026, and I contribute to the academic community as a PC/SPC (reviewer) member for:
📚 Journals
- TOIS: ACM Transactions on Information Systems
- Journal of Data and Information Quality
🏛️ Conferences
- ACM ICTIR 2023, SIGIR 2024, SIGIR 2025, SIGIR 2026
- ECIR 2024, 2025
- WSDM 2026
- ACL Rolling Review (ARR) 2026 (feeds ACL / EMNLP / NAACL / EACL)
📝 Publications
Search, Inspect, Fetch: Exploiting Structure-Aware Boolean Retrieval for Deep-Research Agents Long Project Page
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.
Can It Reach the Generator? Investigating the Survival of Prompt-Injection Attacks in Realistic RAG Settings Long
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.
DiffRetriever: Parallel Representative Tokens for Retrieval with Diffusion Language Models Long
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.
AutoBool: Reinforcement-Learned LLM for Effective Automatic Systematic Reviews Boolean Query Generation Long Live Demo
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).
Corpus Subsampling: Estimating the Effectiveness of Neural Retrieval Models on Large Corpora Long
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.
Starbucks: Improved Training for 2D Matryoshka Embeddings Long
Shengyao Zhuang*, Shuai Wang*, Bevan Koopman and Guido Zuccon. 2024. Starbucks: Improved Training for 2D Matryoshka Embeddings. (Accepted in ECIR2026).
Context Embeddings for Efficient Answer Generation in RAG Long
David Rau*, Shuai Wang*, Hervé Déjean and Stéphane Clinchant. 2024. Context Embeddings for Efficient Answer Generation in RAG. (Accepted in WSDM2025).
Zero-shot Generative Large Language Models for Systematic Review Screening Automation Long
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).
Evaluating Generative Ad Hoc Information Retrieval Long
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).
Zero-shot Generative Large Language Models for Systematic Review Screening Automation Long
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).
Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation Long
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).
Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search? Long
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).
Neural Rankers for Effective Screening Prioritization in Medical Systematic Review Literature Search Long
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).
MeSH Term Suggestion for Systematic Review Literature Search Long
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).
Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search Journal
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.
BERGEN: A Benchmarking Library for Retrieval-Augmented Generation Resource
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).
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation Resource
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).
From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search Resource
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).
The Vulnerability of LLM Rankers to Prompt Injection Attacks Reproduce
Yu Yin, Shuai Wang, Bevan Koopman and Guido Zuccon. 2026. The Vulnerability of LLM Rankers to Prompt Injection Attacks. (Accepted SIGIR-2026).
Beyond Chunk-Then-Embed: A Comprehensive Taxonomy and Evaluation of Document Segmentation Strategies for Information Retrieval Reproduce
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).
Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition Reproduce
Zheng Yao, Shuai Wang and Guido Zuccon. 2025. Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition (Accepted SIGIR-2025).
Reassessing Large Language Model Boolean Query Generation for Systematic Reviews Reproduce
Shuai Wang, Harrisen Scells, Bevan Koopman and Guido Zuccon. 2025. Reassessing Large Language Model Boolean Query Generation for Systematic Reviews. (Accepted SIGIR-2025).
2D Matryoshka Training for Information Retrieval Reproduce
Shuai Wang, Shengyao Zhuang, Bevan Koopman and Guido Zuccon. 2025. 2D Matryoshka Training for Information Retrieval. (Accepted SIGIR-2025).
Balanced Topic Aware Sampling for Effective Dense Retriever: A Reproducibility Study Reproduce
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).
SDR for Systematic Reviews: A Reproducibility Study Reproduce
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).
Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning Short
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).
Evalugator🐊—Rapid, Agile Development and Evaluation of Retrieval Augmented Generation Systems Without Labels Short
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).
Large Language Models for Stemming: Promises, Pitfalls and Failures Short
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).
ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search Short
Shuai Wang, Shengyao Zhuang, Bevan Koopman and Guido Zuccon. 2024. ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search. (Accepted in WWW2025).
MeSH Suggester: A Library and System for MeSH Term Suggestion for Systematic Review Boolean Query Construction Short Live Demo
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).
To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers Short
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).
BERT-based Dense Retrievers Require Interpolation with BM25 for Effective Passage Retrieval Short
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).
IELAB at TREC Deep Learning Track 2021 Notebook
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.