👋 欢迎！
您好！我是王率，昆士兰大学IeLab的博士后研究员和即将毕业的博士生。我在Guido Zuccon教授、Bevan Koopman副教授和Harrisen Scells博士的指导下进行研究。
🎓 学术背景
- 博士学位 (即将毕业) - 昆士兰大学 (2021-2025)
- 工程科学硕士 - 昆士兰大学 (2021)
- 理学学士 - 西澳大利亚大学 (2019)
🔬 研究方向
我的研究重点是信息检索和自然语言处理，特别专注于领域特定应用。我的博士研究专注于医学系统综述的自动化，包括：
- 自动Mesh术语推荐
- 筛选优先级排序
- 种子驱动方法
- 布尔查询构建
我也探索一般性IR和NLP挑战，包括联合搜索RAG系统和排序器融合。
👨🏫 教学与指导
目前担任昆士兰大学INFS7410（信息检索与网络搜索）课程的讲师和课程负责人。曾在2021-2024年期间担任多门课程的助教，包括INFS7410、INFS7205和DATA7901/7902/7903。
我热衷于发现优秀的人才进行合作研究——如果您对研究感兴趣，让我们联系并一起创造精彩的成果！
🌍 行业经验
研究实习生 在Naver Lab Europe（2024年2月-7月），专注于检索增强生成(RAG)的上下文压缩研究。
💼 工作机会
从2025年2月开始，我在昆士兰大学担任博士后研究员；我也在积极寻找学术界和工业界的精彩机会。如果您认为我非常适合您的团队，请随时与我联系！
📰 最新动态
Three Papers Accepted in SIGIR 2025
[Reproducibility Paper]: 2D Matryoshka Training for Information Retrieval; Reassessing Large Language Model Boolean Query Generation for Systematic Reviews; Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition.
Started Postdoc at The University of Queensland
Started a postdoc position in information retrieval and natural language processing at The University of Queensland.
Paper Accepted in WWW 2025
[Short Paper]: ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search
🤝 学术服务
我通过担任以下期刊/会议的审稿人/程序委员会成员为学术社区做贡献：
📚 期刊
- TOIS: ACM信息系统汇刊
- 数据与信息质量期刊
🏛️ 会议
- ACM ICTIR 2023, SIGIR 2024, SIGIR 2025
- ECIR 2024
📝 学术论文
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).
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. (Arxiv Preprint).
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).
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).
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).
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).
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).
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).
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).
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).
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).
MeSH Suggester: A Library and System for MeSH Term Suggestion for Systematic Review Boolean Query Construction Short
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).
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).
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.
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).
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).
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).
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).
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.
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).