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Professor Guido Zuccon
Professor

Guido Zuccon

Email: 
Phone: 
+61 7 336 58319

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

Qualifications

  • Doctor of Philosophy, University of Glasgow

Research interests

  • 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)

  • 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

207 works between 2008 and 2024

1 - 20 of 207 works

Featured

2019

Journal Article

Consumer health search on the web: study of web page understandability and its integration in ranking algorithms

Palotti, Joao, Zuccon, Guido and Hanbury, Allan (2019). Consumer health search on the web: study of web page understandability and its integration in ranking algorithms. Journal of Medical Internet Research, 21 (1) e10986, e10986. doi: 10.2196/10986

Consumer health search on the web: study of web page understandability and its integration in ranking algorithms

Featured

2018

Journal Article

Payoffs and pitfalls in using knowledge-bases for consumer health search

Jimmy, Zuccon, Guido and Koopman, Bevan (2018). Payoffs and pitfalls in using knowledge-bases for consumer health search. Information Retrieval Journal, 22 (3-4), 350-394. doi: 10.1007/s10791-018-9344-z

Payoffs and pitfalls in using knowledge-bases for consumer health search

Featured

2018

Journal Article

Recursive module extraction using louvain and pagerank

Perrin, Dimitri and Zuccon, Guido (2018). Recursive module extraction using louvain and pagerank. F1000Research, 7 (1286) 1286, 1-11. doi: 10.12688/f1000research.15845.1

Recursive module extraction using louvain and pagerank

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

A setwise approach for effective and highly efficient zero-shot ranking with large language models

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

Evaluating generative ad hoc information retrieval

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

Embark on DenseQuest: a system for selecting the best dense retriever for a custom collection

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

Large language models based stemming for information retrieval: promises, pitfalls and failures

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

Revisiting document expansion and filtering for effective first-stage retrieval

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

ACM SIGIR 2024 Chairs' Welcome

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

Leveraging LLMs for unsupervised dense retriever ranking

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

Dense retrieval with continuous explicit feedback for systematic review screening prioritisation

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

FeB4RAG: evaluating federated search in the context of retrieval augmented generation

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

The new paradigm in machine learning – foundation models, large language models and beyond: a primer for physicians

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

CoLAL: Co-learning active learning for text classification

2024

Book Chapter

A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TAR

Mao, Xinyu, Koopman, Bevan and Zuccon, Guido (2024). A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TAR. Lecture Notes in Computer Science. (pp. 132-146) Cham: Springer Nature Switzerland. doi: 10.1007/978-3-031-56066-8_13

A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TAR

2024

Conference Publication

Stochastic Featurization for Active Learning

Le, Linh, Nguyen, Minh-Tien, Tran, Khai Phan, Zhao, Genghong, Xia, Zhang, Zuccon, Guido and Demartini, Gianluca (2024). Stochastic Featurization for Active Learning. Second International Workshop, TAI4H 2024, Jeju, South Korea, 4 August 2024. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-67751-9_5

Stochastic Featurization for Active Learning

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

Zero-shot generative large language models for systematic review screening automation

2024

Book Chapter

How to Forget Clients in Federated Online Learning to Rank?

Wang, Shuyi, Liu, Bing and Zuccon, Guido (2024). How to Forget Clients in Federated Online Learning to Rank?. Lecture Notes in Computer Science. (pp. 105-121) Cham: Springer Nature Switzerland. doi: 10.1007/978-3-031-56063-7_7

How to Forget Clients in Federated Online Learning to Rank?

2023

Journal Article

Why clinical artificial intelligence is (almost) non-existent in Australian hospitals and how to fix it

van der Vegt, Anton, Campbell, Victoria and Zuccon, Guido (2023). Why clinical artificial intelligence is (almost) non-existent in Australian hospitals and how to fix it. Medical Journal of Australia, 220 (4), 172-175. doi: 10.5694/mja2.52195

Why clinical artificial intelligence is (almost) non-existent in Australian hospitals and how to fix it

2023

Conference Publication

Selecting which dense retriever to use for zero-shot search

Khramtsova, Ekaterina, Zhuang, Shengyao, Baktashmotlagh, Mahsa, Wang, Xi and Zuccon, Guido (2023). Selecting which dense retriever to use for zero-shot search. 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, United States: Association for Computing Machinery. doi: 10.1145/3624918.3625330

Selecting which dense retriever to use for zero-shot search

Funding

Current funding

  • 2024 - 2029
    NASCENT: Translating AI research to clinical practice; National infrastructure for real-time clinical AI trials.
    MRFF - National Critical Infrastructure Initiative
    Open grant
  • 2023 - 2024
    From Search to Synthesis: Automating the Systematic Review Creation Process
    Universities Australia - Germany Joint Research Co-operation Scheme
    Open grant
  • 2022 - 2026
    Digital Infrastructure For improving First Nations Maternal & Child Health
    MRFF Research Data Infrastructure
    Open grant
  • 2020 - 2025
    Enhanced data extraction and modelling from electronic medical records and phenotyping for clinical care, and research: Case studies in management of medication stewardship
    Digital Health CRC
    Open grant

Past funding

  • 2022 - 2023
    Federated Online Learning of Neural Rankers
    CCF Baidu Pinecone Fund
    Open grant
  • 2022 - 2023
    SMART Project - Towards Systematic Maturation of Analytics and System Redesign to Transform (SMART) Healthcare and Public Health Research
    Queensland Health
    Open grant
  • 2021 - 2023
    Enhancing the Capability of the Global Policing Database
    Australian Federal Police
    Open grant
  • 2021 - 2023
    Enhancing the Capability of the Global Policing Database
    Australia and New Zealand Society of Evidence Based Policing Inc
    Open grant
  • 2021 - 2024
    AI-driven Effective Query Formulation for Better Systematic Reviews
    ARC Discovery Projects
    Open grant
  • 2021 - 2022
    Tribu: AI-based Ticketing System
    Innovation Connections
    Open grant
  • 2020 - 2022
    AgAsk: A machine learning generated question-answering conversational agent for data-driven growing decisions.
    Grains Research & Development Corporation
    Open grant
  • 2019 - 2021
    Development of Explainable AI Techniques for Complex Disease Diagnosis using Genomics Data (SPARC grant led by Symbiosis International)
    Symbiosis International Deemed University
    Open grant
  • 2019 - 2022
    Collaborative Lab of Health Informatics with Neusoft
    Neusoft Research of Intelligent Healthcare Technology, Co Ltd
    Open grant
  • 2018 - 2021
    Searching when the stakes are high: better health decisions from Dr Google
    ARC Discovery Early Career Researcher Award
    Open grant
  • 2018 - 2022
    Searching for Better Health
    Google Inc
    Open grant

Supervision

Availability

Professor Guido Zuccon is:
Available for supervision

Before you email them, read our advice on how to contact a supervisor.

Supervision history

Current supervision

  • Doctor Philosophy

    AI-driven Effective Query Formulation for Better Systematic Reviews

    Principal Advisor

  • Doctor Philosophy

    AI-driven Effective Query Formulation for Better Systematic Reviews

    Principal Advisor

  • Doctor Philosophy

    AgAsk: A question-answering conversational agent for data-driven growing decisions

    Principal Advisor

  • Doctor Philosophy

    Automated Evaluation of Intelligent Conversational Agents

    Principal Advisor

  • Doctor Philosophy

    Large Language Models for Search

    Principal Advisor

  • Doctor Philosophy

    Data Mining on Many-to-Many Complex Relationships

    Principal Advisor

    Other advisors: Professor Xue Li

  • Doctor Philosophy

    Summarisation of Healthcare Information

    Principal Advisor

  • Doctor Philosophy

    Information Retrieval for Precision Medicine

    Principal Advisor

  • Doctor Philosophy

    Efficient Next-Generation Information Retrieval Systems

    Associate Advisor

    Other advisors: Associate Professor Gianluca Demartini, Dr Joel Mackenzie

  • 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

  • Doctor Philosophy

    Exploring Facets of Model Generalizability on Out-of-Distribution Data

    Associate Advisor

    Other advisors: Dr Mahsa Baktashmotlagh

  • Doctor Philosophy

    Development of small animal emergency medicine clinical recommender systems to improve veterinary clinical decision-making and care

    Associate Advisor

    Other advisors: Professor Ricardo Soares Magalhaes

Completed supervision

Media

Enquiries

For media enquiries about Professor Guido Zuccon's areas of expertise, story ideas and help finding experts, contact our Media team:

communications@uq.edu.au