The Information Engineering Lab

The ielab is a collaborative group of researchers working in the area of information engineering. Much of this research is specifically on the areas of information retrieval, i.e. search engine technology and information seeking, data science, and health informatics.

Highlighted Projects

A full list of projects is available at the projects page, including associated resources (software, data).

To access a list of our publications, please visit the publications page; this often includes a pre-print version of each publication.

For advertised student projects (including PhD, masters, and undergraduate) please visit the student projects page.


'Causality Discovery with Domain Knowledge for Drug-Drug Interactions Discovery' accepted at ADMA'19

02 August 2019

The conference paper titled ‘Causality Discovery with Domain Knowledge for Drug-Drug Interactions Discovery’ has been accepted as a full paper for publication at The 15th International Conference on Advanced Data Mining and Applications 2019. Three of the authors (including the first author) of the paper are members of ielab’s research team: Sitthichoke Subpaiboonkit, Harry Scells, and Guido Zuccon. An abstract and pre-print version of the accepted paper have been made available at the publication page:

Four publications accepted from the ielab research group at SIGIR'19

21 July 2019

This year, the ielab research group have had the following research papers accepted at the International ACM SIGIR Conference on Research and Development in Information Retrieval 2019.

ielab at the Cochrane Colloquium 2019

01 July 2019

Two abstracts about Boolean query visualisation and automatic Boolean query refinement have been accepted for publication and presentation at the 2019 Cochrane Colloquium.

3 Fully Funded PhD Scholarships in Health Data Science at UQ

06 February 2019

Fully funded PhD scholarships are available at the Data Science group, University of Queensland for the following three projects (for start in April or July 2019). Application deadline 15/02/2019.

'Consumer Health Search on the Web: Study of Web Page Understandability and Its Integration in Ranking Algorithms' accepted at JMIR

30 January 2019

The journal paper titled ‘Consumer Health Search on the Web: Study of Web Page Understandability and Its Integration in Ranking Algorithms’ has been accepted for publication in the Journal of Medical Internet Research (JMIR). Authors of the paper are Joao Palotti, Guido Zuccon, and Allan Hanbury. Please visit the JMIR page to read and download.


ielab’s core group includes Information Retrieval researchers at the University of Queensland (UQ) in Brisbane, Australia. However, the group has expanded to include researchers from the CSIRO, University of Strathclyde, Qatar Computing Research Institute and more.

Researcher Staff

Doctoral Students (PhD)

PhD student, UQ & CSIRO, Bridging the Human-Task Cognitive Gap. A Theoretical Framework Applied to Medical Search.
PhD student, UQ, Exploring methods for improving the effective information retrieval of legal documents.
PhD student, UQ & CSIRO, Improving Systematic Review Creation with Information Retrieval.
PhD student, UQ & UBAYA, Search Engines that allow the General Public to Make Well-Informed Health Decisions.
PhD student, UQ, Insights Recommendation for Exploring IoT Data.
PhD student, UQ, Evaluation of Medical Chatbots.
PhD student, UQ, Online Learning to Rank.
PhD student, University of Queensland (UQ) & Chiang Mai University (CMU), Causality Discovery in drug-drug interaction.
PhD student, UQ, Similarity Computing on Complex Health Data.

External members

Research Associate, Qatar Computing Research Institute (QCRI)
Associate Professor and Chancellor's Fellow, University of Strathclyde (UK).

Honours Students

  • [completed 2017] Liam Cripwell. Honours student, QUT: Generating Clinical Queries from Patient Narratives
  • [completed 2016] Harrisen Scells. Honours student, QUT: Investigating Methods Of Annotating Lifelogs For Use In Search

Masters Students

[completed 2017]:

  • Doyoo Baek: A predictive analysis of heavy machinery deterioration (Industrial CEED collaboration with Hastings Deering Pty Ltd)
  • Xiaoran Chu: OMG! The Amazing Result of Using Machine Learning to Build Classifiers for Clickbait Detection
  • Linni Qin: Forecating Zestimate Error
  • Zhiying Zhou: Educational Data Mining: Analyze and Predict student’s academic performance
  • Harmandeep Kaur Bhullar: Data Analysis of an European Soccer Database
  • Harshita Jain: An Analysis of Spam SMS Features
  • Davinder Kaur: Symptoms of Lower Back Pain - A Data Analysis and Research Project