The Data Science discipline conducts world-leading research and develops innovative and practical solutions for business, scientific and social applications in the realm of big data. Research Themes include: data quality management, information resilience, machine learning, computer vision, trajectory computing, AI models and applications, data analytics, data mining and predictive analytics, big data intelligence, micro artificial intelligence, health intelligence information retrieval and web search.

Website

https://eecs.uq.edu.au/data-science

Organisation type

University Research Centre

Number of research staff

20-100 research staff

Address

General Purpose South Building 78, Staff House Road, The University of Queensland, St Lucia QLD 4072

Strengths and capabilities

• Advanced data curation processes for responsible data management
• Designing novel deep learning approaches and scalable and adaptable big data platforms
• Algorithms for enhanced data protection and surveillance
• Developing algorithms to build predictive and graph-based computational models
• Leveraging social media and health data to train effective and powerful supervised models
• Manipulating and analysing large-scale complex data across various domains
• Developing innovative data mining and machine learning techniques for bias mitigation
• AI applications based on multiple data sources and data fusion for health and social analytics
• Creating next-generation technology for large data repositories with a focus on computer vision, information retrieval and health data science.

Facilities and major equipment

• High-performance computing clusters
• Dedicated data storage systems
• Secure data processing facility

Lead researchers

Prof. Helen Huang

• 2014: Received Women in Technology (WiT) Infotech Research Award, Queensland.
• 2016: Awarded Chris Wallace Award by CORE Australasia for significant contributions in Computer Science.
• 2014: Became an ARC Future Fellow at the University of Queensland, School of EECS.
• 2018: Recipient of UQ's Excellence in Higher Degree by Research Supervision Award.
• 2021: Chair, iCORE Conference rankings in the field of Computer Vision and Multimedia Computation
• 2022: Member of the Australian Research Council (ARC) College of Experts
• 2022: Chair, The Computing Research and Education Association of Australasia (CORE) Award for Outstanding Research

Current: Data Science Discipline Leader at UQ, leading landmark interdisciplinary studies spanning civil engineering, smart water management, agriculture, and advanced manufacturing in Australia, earning >$20 M in investment funding.

Prof. Shazia Sadiq FTSE

• 2017: Awarded BPM Test of Time award for her 2007 paper at the Int Conf on Business Process Management.
• 2018: Spearheaded UQ’s Master of Data Science, addressing Queensland's skill shortage in data science.
• 2020: Launched ARC ITTC for Information Resilience, securing over $10 million in research funding.

Professor Guido Zuccon

• 2018-2020: ARC DECRA Fellow focusing on Health Data Science and Information Retrieval.
• Current: Professorial Research Fellow at UQ, AI Director at Queensland Digital Health Centre (QDHeC), and Affiliate Professor at UQ Centre for Health Services Research. Leads the Information Engineering Lab (ielab).
• Awards: Best paper at AIRS 2017 and leadership roles including Consumer Health Search task leader for CLEF eHealth Evaluation Lab since 2014, and TREC 2019 Decision Track organizer.
• Funding: Secured research support from ARC, Google, Microsoft, and CSIRO.

Achievements of the centre

Awards and Honors

• IEEE Computer Society’s AI’s 10 to Watch 2022: Recognition for outstanding researchers in artificial intelligence.
• Young Tall Poppy Science Awards 2023: Awarded for excellence in scientific research and communication.
• Field Leader of Data Mining & Analysis by The Australian's Research 2020 Magazine: Identified as leading contributors in the field.
• AI 2000 Most Influential Scholar Honorable Mention in Data Mining (2022, 2023, 2024): Acknowledgment for significant contributions to the field of data mining.
• 2023 National Transport Research Award for Local Government Innovation, Australia – for recognising innovative collaborative research that is conducted with the Logan City Council on adopting artificial intelligence technologies in road asset management.

Best Paper Awards

• ACM Multimedia 2023 Best Student Award for advancements in multimedia computing.
• WSDM 2023 Best Paper Award - Honorable Mention for advancements in web search and data mining.
• ICDE 2019 Best Paper Award for contributions to engineering and database technology.
• DASFAA 2020 Best Student Paper Award for developments in database systems for advanced applications.
• ICDM 2018 Best Paper Nomination for innovations in data mining and data analysis.

Significant Fellowships

• 3 Australian Research Council Future Fellowships 2021, 2023, and 2024: Funded for pioneering research in predictive analytics and graph learning.
• 3 Discovery Early Career Researcher Awards 2023, and 2024: Support for early-career researchers to establish their research in big data and analytics.

Commercial Success

• Leadership in the ARC Industry Transformation Training Centre on Information Resilience, which delivers advanced data security solutions.
• The ARC Centre of Excellence for Mathematical and Statistical Frontiers has converted complex algorithms into tools for logistics and decision-making now used by industry leaders.
• Collaborations with tech giants like Google and Microsoft have produced widely adopted cloud-based analytics tools.
• The discipline's advancements in health data analytics have led to predictive tools that improve healthcare management.
• UQ's involvement in Smart Cities projects has integrated IoT and AI technologies into urban management, enhancing sustainability and efficiency.

Research Influence

• Faculty within the discipline have published over 300 research papers in highly regarded venues, such as the CVPR, MM, KDD, SIGIR, WWW, VLDB and ICDE, with a significant number of these publications being classified as CORE A* and CCF A, demonstrating high research quality and impact.

Key science sectors

More information about the sectors this centre is involved in:

• Digital economy