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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

81 - 100 of 207 works

2021

Conference Publication

Federated Online Learning to Rank with Evolution Strategies: A Reproducibility Study

Wang, Shuyi, Zhuang, Shengyao and Zuccon, Guido (2021). Federated Online Learning to Rank with Evolution Strategies: A Reproducibility Study. 43rd European Conference on IR Research, ECIR 2021, Online, 28 March – 1 April 2021. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-72240-1_10

Federated Online Learning to Rank with Evolution Strategies: A Reproducibility Study

2021

Conference Publication

Deep query likelihood model for information retrieval

Zhuang, Shengyao, Li, Hang and Zuccon, Guido (2021). Deep query likelihood model for information retrieval. The 43rd European Conference On Information Retrieval (ECIR), Lucca, Italy - online event, March 28–April 1, 2021. Cham, Switzerland: Elsevier. doi: 10.1007/978-3-030-72240-1_49

Deep query likelihood model for information retrieval

2021

Conference Publication

User models, metrics and measures of search: a tutorial on the C/W/L evaluation framework

Azzopardi, Leif, Moffat, Alistair, Thomas, Paul and Zuccon, Guido (2021). User models, metrics and measures of search: a tutorial on the C/W/L evaluation framework. 6th ACM SIGIR Conference on Human Information Interaction and Retrieval, CHIIR 2021, Virtual, 14 - 19 March 2021. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3406522.3446049

User models, metrics and measures of search: a tutorial on the C/W/L evaluation framework

2021

Journal Article

Do better search engines really equate to better clinical decisions? If not, why not?

van der Vegt, Anton, Zuccon, Guido and Koopman, Bevan (2021). Do better search engines really equate to better clinical decisions? If not, why not?. Journal of the Association for Information Science and Technology, 72 (2) asi.24398, 141-155. doi: 10.1002/asi.24398

Do better search engines really equate to better clinical decisions? If not, why not?

2021

Conference Publication

TILDE: Term Independent Likelihood moDEl for passage re-ranking

Zhuang, Shengyao and Zuccon, Guido (2021). TILDE: Term Independent Likelihood moDEl for passage re-ranking. The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Online, 11-15 July 2021. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3404835.3462922

TILDE: Term Independent Likelihood moDEl for passage re-ranking

2021

Conference Publication

Diagnosis ranking with knowledge graph convolutional networks

Liu, Bing, Zuccon, Guido, Hua, Wen and Chen, Weitong (2021). Diagnosis ranking with knowledge graph convolutional networks. 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28-April 1 2021. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-030-72113-8_24

Diagnosis ranking with knowledge graph convolutional networks

2021

Conference Publication

How do online learning to rank methods adapt to changes of intent?

Zhuang, Shengyao and Zuccon, Guido (2021). How do online learning to rank methods adapt to changes of intent?. The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Online, 11-15 July. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3404835.3462937

How do online learning to rank methods adapt to changes of intent?

2021

Conference Publication

Discriminative features generation for mortality prediction in ICU

Pokharel, Suresh, Shi, Zhenkun, Zuccon, Guido and Li, Yu (2021). Discriminative features generation for mortality prediction in ICU. International Conference on Advanced Data Mining and Applications, Foshan, China, 12-14 November 2020. Cham, Switzerland: Springer . doi: 10.1007/978-3-030-65390-3_25

Discriminative features generation for mortality prediction in ICU

2020

Journal Article

A comparison of automatic Boolean query formulation for systematic reviews

Scells, Harrisen, Zuccon, Guido and Koopman, Bevan (2020). A comparison of automatic Boolean query formulation for systematic reviews. Information Retrieval Journal, 24 (1), 3-28. doi: 10.1007/s10791-020-09381-1

A comparison of automatic Boolean query formulation for systematic reviews

2020

Journal Article

How searching under time pressure impacts clinical decision making

Van der Vegt, Anton, Zuccon, Guido, Koopman, Bevan and Deacon, Anthony (2020). How searching under time pressure impacts clinical decision making. Journal of the Medical Library Association, 108 (4), 564-573. doi: 10.5195/jmla.2020.915

How searching under time pressure impacts clinical decision making

2020

Journal Article

Temporal tree representation for similarity computation between medical patients

Pokharel, Suresh, Zuccon, Guido, Li, Xue, Utomo, Chandra Prasetyo and Li, Yu (2020). Temporal tree representation for similarity computation between medical patients. Artificial Intelligence in Medicine, 108 101900, 101900. doi: 10.1016/j.artmed.2020.101900

Temporal tree representation for similarity computation between medical patients

2020

Conference Publication

Systematic review automation tools for end-to-end query formulation

Li, Hang, Scells, Harrisen and Zuccon, Guido (2020). Systematic review automation tools for end-to-end query formulation. SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval, Virtual, July 2020. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3397271.3401402

Systematic review automation tools for end-to-end query formulation

2020

Journal Article

Learning inter-sentence, disorder-centric, biomedical relationships from medical literature

van der Vegt, Anton H., Zuccon, Guido and Koopman, Bevan (2020). Learning inter-sentence, disorder-centric, biomedical relationships from medical literature. AMIA Annual Symposium. Proceedings, 2019, 1216-1225.

Learning inter-sentence, disorder-centric, biomedical relationships from medical literature

2020

Conference Publication

You can teach an old dog new tricks: Rank fusion applied to coordination level matching for ranking in systematic reviews

Scells, Harrisen, Zuccon, Guido and Koopman, Bevan (2020). You can teach an old dog new tricks: Rank fusion applied to coordination level matching for ranking in systematic reviews. 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, 14–17 April, 2020 . Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-030-45439-5_27

You can teach an old dog new tricks: Rank fusion applied to coordination level matching for ranking in systematic reviews

2020

Conference Publication

Sampling query variations for learning to rank to improve automatic Boolean query generation in systematic reviews

Scells, Harrisen, Zuccon, Guido, Sharaf, Mohamed A. and Koopman, Bevan (2020). Sampling query variations for learning to rank to improve automatic Boolean query generation in systematic reviews. WWW '20: The Web Conference 2020, Taipei, Taiwan, April 2020. New York, United States: Association for Computing Machinery. doi: 10.1145/3366423.3380075

Sampling query variations for learning to rank to improve automatic Boolean query generation in systematic reviews

2020

Conference Publication

A computational approach for objectively derived systematic review search strategies

Scells, Harrisen, Zuccon, Guido, Koopman, Bevan and Clark, Justin (2020). A computational approach for objectively derived systematic review search strategies. 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, 14–17 April, 2020. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-030-45439-5_26

A computational approach for objectively derived systematic review search strategies

2020

Conference Publication

Representing EHRs with temporal tree and sequential pattern mining for similarity computing

Pokharel, Suresh, Zuccon, Guido and Li, Yu (2020). Representing EHRs with temporal tree and sequential pattern mining for similarity computing. 16th International Conference, ADMA 2020, Foshan, China, 12-14 November 2020. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-030-65390-3_18

Representing EHRs with temporal tree and sequential pattern mining for similarity computing

2020

Conference Publication

Quality matters: understanding the impact of incomplete data on visualization recommendation

Mafrur, Rischan, Sharaf, Mohamed A. and Zuccon, Guido (2020). Quality matters: understanding the impact of incomplete data on visualization recommendation. 31st International Conference, DEXA 2020, Bratislava, Czech Republic, 14–17 September, 2020. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-030-59003-1_8

Quality matters: understanding the impact of incomplete data on visualization recommendation

2020

Conference Publication

Counterfactual online learning to rank

Zhuang, Shengyao and Zuccon, Guido (2020). Counterfactual online learning to rank. 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14–17, 2020. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-030-45439-5_28

Counterfactual online learning to rank

2020

Conference Publication

Automatic Boolean query formulation for systematic review literature search

Scells, Harrisen, Zuccon, Guido, Koopman, Bevan and Clark, Justin (2020). Automatic Boolean query formulation for systematic review literature search. WWW '20: The Web Conference 2020, Taipei, Taiwan, April 2020. New York, United States: Association for Computing Machinery. doi: 10.1145/3366423.3380185

Automatic Boolean query formulation for systematic review literature 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
  • 2021 - 2024
    AI-driven Effective Query Formulation for Better Systematic Reviews
    ARC Discovery Projects
    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 - 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

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Supervision history

Current supervision

  • Doctor Philosophy

    Large Language Models for Search

    Principal Advisor

  • Doctor Philosophy

    Summarisation of Healthcare Information

    Principal Advisor

  • Doctor Philosophy

    Information Retrieval for Precision Medicine

    Principal Advisor

  • Doctor Philosophy

    AI-driven Effective Query Formulation for Better Systematic Reviews

    Principal Advisor

  • Doctor Philosophy

    Data Mining on Many-to-Many Complex Relationships

    Principal Advisor

    Other advisors: Professor Xue Li

  • 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

    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

  • Doctor Philosophy

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

    Associate Advisor

    Other advisors: Dr Mahsa Baktashmotlagh

  • 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

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