Overview
Background
Professor Guido Zuccon is a Professorial Research Fellow at The University of Queensland, Electrical Engineering and Computer Science School, the AI DIrector for the Queensland Digital Health Centre (QDHeC), an Affiliate Professor at the UQ Centre for Health Services Research, Faculty of Medicine, and an Honorary Reader at Strathclyde University (UK). He leads the Information Engineering Lab (ielab), a research team working in Information Retrieval and Health Data Science. He was an ARC DECRA Fellow (2018-2020).
Guido's main research interests are Information Retrieval, Health Search, Formal Models of Search and Search Interaction, and Health Data Science. He has successfully attracted funding from the ARC via an ARC Discovery Early Career Research Award Fellowship and an ARC Discoverty Project. His research has also been funded by Google (Google Research Awards program), Grain Research and Development Corporation (GRDC), Microsoft (Microsoft Azure for Research Award), the CSIRO (research gifts and PhD Students Top-up scholarships), the Australian Academy of Science (FASIC program), the European Science Foundation, and Neusoft Corporation.
Guido has published more than 100 peer-reviewed research articles at conferences and in journals, in information retrieval and health informatics; of these more than 30 are ranked in the top 10% of his filed (field weighted average) and more than 10 are ranked in the top 1%. He has won best papers award at AIRS 2017 (“Automatic Query Generation from Legal Texts for Case Law Retrieval”), CLEF 2016 (“Assessors agreement: A case study across assessor type, payment levels, query variations and relevance dimensions”), ALTA 2015 (“Analysis of Word Embeddings and Sequence Features for Clinical Information Extraction”), and ECIR 2012 (“Top-k retrieval using facility location analysis”). His research on people using search engines to seek health advice on the web has been widely disseminated by the media (190+ national and international newspaper articles, 10+ TV and radio interviews in 2015; see the media page coverage for that project). Guido is the Consumer Health Search task leader for the CLEF eHealth Evaluation Lab, since 2014. He is one of the TREC 2019 Decision Track organisers: this is an international evaluation effort in Information Retrieval that aims to investigate how people use search engines to make decisions (with a focus in 2019 on consumer health search). Guido has provided scientific tutorials to other researchers in his field at ACM SIGIR 2015 and 2018, ACM CIKM 2015, ACM ICTIR 2016, RUSSIR 2018, WSDM 2019.
Guido has reviewed for top journals and conferences in his field, including ACM TOIS, FnTIR, JASIST, IRJ, ACM TIST, ACM TWEB, IP&M, ACM SIGIR,ACM CIKM, ACM ICTIR, ACM WSDM, WWW, ECIR, ACM SIG-PODS. He was awarded the Best Reviewer Award at ECIR 2014. He has served as general chair, program chair, workshop chair and publicity chair for conferences in his research field, including ADCS (either PC Chair or General Chair in 2013, 2014, 2017), AIRS 2015 (General Chair), ECIR 2015 (Workshop Chair) and WSDM 2019 (publicity chair). Dr Zuccon is the Information co-Director for ACM SIGIR and was one of the recognised IR leaders invited to participate to the 3rd Strategic Workshop in Information Retrieval (SWIRL III, 2018).
Before joining the University of Queensland, Guido was a Lecturer (2014-2017) and Senior Lecturer (2017-2018) at the Queensland University of Technology, Australia, and a Post Doctoral Research Fellow at the Australian E-Health Research Centre (AEHRC), CSIRO (2011-2014), Australia. He received a Ph.D. in Computer Science at the University of Glasgow, UK (2012), focusing on Formal Models of Information Retrieval based on Quantum Theory, and a M.Comp.Eng. summa cum laude at the University of Padova, Italy (2007).
Availability
- Professor Guido Zuccon is:
- Available for supervision
Fields of research
Qualifications
- Doctor of Philosophy, University of Glasgow
Research interests
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Formal models of Information Retrieval
Models of search, information seeking and interactions; Semantic models of search; Exploiting word embeddings and deep learning in Information Retrieval; Evaluation of Information Retrieval (including task-based evaluation)
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Medical/Health Information Retrieval and Data Science
Retrieval models and strategies for consumers searching the web for health advise; Retrieval models and strategies for cohort identification for clinical trials from electronic medical records; Retrieval models and strategies for clinical decision support and evidence-based medicine; Models, approaches and strategies for automating systematic reviews, in particular with respect to the search phase health search evaluation; Semantic models for health data science, including automatic and bootstrapped generation and exploitation of health knowledge graph
Works
Search Professor Guido Zuccon’s works on UQ eSpace
2025
Conference Publication
Set-Encoder: Permutation-Invariant Inter-passage Attention for Listwise Passage Re-ranking with Cross-Encoders
Schlatt, Ferdinand, Fröbe, Maik, Scells, Harrisen, Zhuang, Shengyao, Koopman, Bevan, Zuccon, Guido, Stein, Benno, Potthast, Martin and Hagen, Matthias (2025). Set-Encoder: Permutation-Invariant Inter-passage Attention for Listwise Passage Re-ranking with Cross-Encoders. 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, 6-10 April 2025. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-88711-6_1
2025
Conference Publication
DenseReviewer: A Screening Prioritisation Tool for Systematic Review Based on Dense Retrieval
Mao, Xinyu, Leelanupab, Teerapong, Scells, Harrisen and Zuccon, Guido (2025). DenseReviewer: A Screening Prioritisation Tool for Systematic Review Based on Dense Retrieval. 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, 6-10 April 2025. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-88720-8_11
2025
Other Outputs
LLM-VPRF: Large language model based vector pseudo relevance feedback
Li, Hang, Zhuang, Shengyao, Koopman, Bevan and Zuccon, Guido (2025). LLM-VPRF: Large language model based vector pseudo relevance feedback. doi: 10.48550/arXiv.2504.01448
2025
Conference Publication
Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-ranking
Schlatt, Ferdinand, Fröbe, Maik, Scells, Harrisen, Zhuang, Shengyao, Koopman, Bevan, Zuccon, Guido, Stein, Benno, Potthast, Martin and Hagen, Matthias (2025). Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-ranking. 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, 6-10 April 2025. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-88714-7_31
2024
Conference Publication
Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems
Zhuang, Shengyao, Koopman, Bevan, Chu, Xiaoran and Zuccon, Guido (2024). Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems. 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, Tokyo, Japan, 9-12 December 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3673791.3698414
2024
Conference Publication
Searching in Professional Instant Messaging Applications: User Behaviour, Intent, and Pain-points
Sabei, Ismail, Galal, Mahmoud, Koopman, Bevan and Zuccon, Guido (2024). Searching in Professional Instant Messaging Applications: User Behaviour, Intent, and Pain-points. 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, Tokyo, Japan, 9-12 December 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3673791.3698417
2024
Other Outputs
TPRF: A transformer-based pseudo-relevance feedback model for efficient and effective retrieval
Li, Hang, Yu, Chuting, Mourad, Ahmed, Koopman, Bevan and Zuccon, Guido (2024). TPRF: A transformer-based pseudo-relevance feedback model for efficient and effective retrieval. doi: 10.48550/arXiv.2401.13509
2024
Conference Publication
PromptReps: prompting large language models to generate dense and sparse representations for zero-shot document retrieval
Zhuang, Shengyao, Ma, Xueguang, Koopman, Bevan, Lin, Jimmy and Zuccon, Guido (2024). PromptReps: prompting large language models to generate dense and sparse representations for zero-shot document retrieval. 29th Conference on Empirical Methods in Natural Language Processing, Miami, FL USA, 12-16 November 2024. Stroudsberg, PA USA: Association for Computational Linguistics. doi: 10.18653/v1/2024.emnlp-main.250
2024
Conference Publication
Source-Free Domain-Invariant Performance Prediction
Khramtsova, Ekaterina, Baktashmotlagh, Mahsa, Zuccon, Guido, Wang, Xi and Salzmann, Mathieu (2024). Source-Free Domain-Invariant Performance Prediction. 18th European Conference on Computer Vision ECCV 2024, Milan, Italy, 29 September – 4 October 2024. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-72989-8_6
2024
Conference Publication
Dense retrieval with continuous explicit feedback for systematic review screening prioritisation
Mao, Xinyu, Zhuang, Shengyao, Koopman, Bevan and Zuccon, Guido (2024). Dense retrieval with continuous explicit feedback for systematic review screening prioritisation. 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: 10.1145/3626772.3657921
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
A setwise approach for effective and highly efficient zero-shot ranking with large language models
Zhuang, Shengyao, Zhuang, Honglei, Koopman, Bevan and Zuccon, Guido (2024). A setwise approach for effective and highly efficient zero-shot ranking with large language models. SIGIR ’24, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657813
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
Conference Publication
Embark on DenseQuest: a system for selecting the best dense retriever for a custom collection
Khramtsova, Ekaterina, Leelanupab, Teerapong, Zhuang, Shengyao, Baktashmotlagh, Mahsa and Zuccon, Guido (2024). Embark on DenseQuest: a system for selecting the best dense retriever for a custom collection. 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: 10.1145/3626772.3657674
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
Revisiting document expansion and filtering for effective first-stage retrieval
Mansour, Watheq, Zhuang, Shengyao, Zuccon, Guido and Mackenzie, Joel (2024). Revisiting document expansion and filtering for effective first-stage retrieval. SIGIR '24, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657850
2024
Journal Article
ACM SIGIR 2024 Chairs' Welcome
Hui Yang, Grace, Wang, Hongning, Han, Sam, Hauff, Claudia, Zuccon, Guido and Zhang, Yi (2024). ACM SIGIR 2024 Chairs' Welcome. SIGIR 2024 - Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, iii-v. doi: 10.1145/3626772
2024
Conference Publication
Leveraging LLMs for unsupervised dense retriever ranking
Khramtsova, Ekaterina, Zhuang, Shengyao, Baktashmotlagh, Mahsa and Zuccon, Guido (2024). Leveraging LLMs for unsupervised dense retriever ranking. 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: 10.1145/3626772.3657798
2024
Journal Article
The new paradigm in machine learning – foundation models, large language models and beyond: a primer for physicians
Scott, Ian A. and Zuccon, Guido (2024). The new paradigm in machine learning – foundation models, large language models and beyond: a primer for physicians. Internal Medicine Journal, 54 (5), 705-715. doi: 10.1111/imj.16393
2024
Conference Publication
CoLAL: Co-learning active learning for text classification
Le, Linh, Zhao, Genghong, Zhang, Xia, Zuccon, Guido and Demartini, Gianluca (2024). CoLAL: Co-learning active learning for text classification. Thirty-Eighth AAAI Conference on Artificial Intelligence, Vancouver, BC Canada, 20–27 February 2024. Washington, DC United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v38i12.29235
Funding
Current funding
Supervision
Availability
- Professor Guido Zuccon is:
- Available for supervision
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Supervision history
Current supervision
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Doctor Philosophy
Methods for the Effective Retrieval of Chat Conversations
Principal Advisor
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Doctor Philosophy
Symptom Checkers, Search Engines and Conversational Agents for Online Health Information Seeking
Principal Advisor
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Doctor Philosophy
Large Language Models for Search
Principal Advisor
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Doctor Philosophy
AI-driven Automated Systematic Reviews
Principal Advisor
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Doctor Philosophy
Data Mining on Many-to-Many Complex Relationships
Principal Advisor
Other advisors: Professor Xue Li
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Doctor Philosophy
AI-driven Effective Query Formulation for Better Systematic Reviews
Principal Advisor
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Doctor Philosophy
Advanced Query Representation and Feedback Methods for Neural Information Retrieval
Principal Advisor
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Doctor Philosophy
What Generators Want from Search: Aligning Retrieval with the Needs of Generative AI
Principal Advisor
Other advisors: Mr Anton Van Der Vegt
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Doctor Philosophy
Human-centered verification of language model outputs
Associate Advisor
Other advisors: Dr Joel Mackenzie, Professor Tim Miller
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Doctor Philosophy
Optimizing Large Language Models for Healthcare Question Answering
Associate Advisor
Other advisors: Dr Teerapong Leelanupab
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Doctor Philosophy
Sample sub-group algorithm bias analysis for machine learning evaluation in the clinical domain
Associate Advisor
Other advisors: Professor Clair Sullivan, Dr Andrew Mallett, Mr Anton Van Der Vegt
Completed supervision
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2023
Doctor Philosophy
Teaching Pre-trained Language Models to Rank Effectively, Efficiently, and Robustly
Principal Advisor
Other advisors: Dr Joel Mackenzie
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2023
Doctor Philosophy
Recommending Data Visualizations: Tackling Diversification and Data Quality Challenges
Principal Advisor
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2022
Doctor Philosophy
Electronic Health Record Representation for Similarity Computing
Principal Advisor
Other advisors: Professor Xue Li
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2020
Doctor Philosophy
Minimal interaction Information Retrieval: A theoretical framework with applications in clinical decision support
Principal Advisor
Other advisors: Professor Xue Li
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2020
Doctor Philosophy
Search Engines that Help People Make Better Health Decisions
Principal Advisor
Other advisors: Professor Gianluca Demartini
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2024
Doctor Philosophy
Exploring Facets of Model Generalizability on Out-of-Distribution Data
Associate Advisor
Other advisors: Associate Professor Mahsa Baktashmotlagh
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2024
Doctor Philosophy
A Study of Active Learning for Named Entity Recognition
Associate Advisor
Other advisors: Professor Gianluca Demartini
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Media
Enquiries
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