Author: Kath Williamson

Congratulations to School of EECS, Professor Helen Huang, and CIRES CI, Dr Sen Wang, for the successful $5M funding of the ARC Training Centre in Predictive Breeding for Agricultural Futures. The Centre will train the next generation of breeders and develop cutting-edge predictive breeding technologies in partnership with industry. The Centre will train 31 PhDs, seven Postdocs and perform research across 21 agriculturally important species. The Centre brings together significant contributions from five Australian universities and 30 national and international partner organisations, and now the ARC, to a total value of $136M.

Congratulations to CIRES Chief Investigator Associate Professor Gianluca Demartini and fellow authors who received the Best Paper Award for their work “Perspectives on Large Language Models for Relevance Judgment” https://lnkd.in/gmf3qU97

The work is the result of an international collaboration between researchers in Italy, USA, Canada, Australia, Germany, Netherlands, and Japan, who highlight the risks and opportunities of large language models and their impact on information retrieval research. The award was given by a committee of information retrieval experts at the ACM, Association for Computing Machinery‘s SIGIR International Conference on the Theory of Information Retrieval in Taipei last week.

We have a number of reading groups happening across the Centre and it’s a key part of our cross-collaboration between our university partners Swinburne University of Technology and The University of Queensland.

Great conversation in the Computational Social Science Reading group led by CIRES Postdoc Dr Hui Yin, PhD researchers Pa Pa Khin and Lufan Zhang, and Dr Javad Pool, around AI-enabled knowledge sharing and learning.

Swinburne University of Technology, hosted our second Computational Social Science Reading group with the collaboration of colleagues at the University of Queensland, part of the ARC Training Centre for Information Resilience (CIRES).

Dr. Hui Yin leads the conversation today with a focus on a paper about knowledge management titled ” AI-enabled knowledge sharing and learning: redesigning roles and processes” (https://lnkd.in/g8xSTg3c). Pa Pa Khin gave a detailed introduction to this paper. This research paper provides a comprehensive framework for analyzing AI’s impact on various practices in knowledge management, and it uncovers the vital necessity of tailored AI-enabled knowledge management systems to cater to modern knowledge worker demands. We had a productive conversation about the role of AI in facilitating organizational knowledge sharing and learning, and how it can support knowledge management activities.

On 6 June, Hon Ed Husic MP, Minister for Industry and Science, launched the NSTC Rapid Response on Generative AI: https://lnkd.in/gfbb5f8c. The development of the report was drawn together through the National Science and Technology Council, guided by its Executive Officer and Australia’s Chief Scientist, Dr Cathy Foley . The work was led by Professors Genevieve Bell, Jean Burgess, Shazia Sadiq and Julian Thomas, drawing on critical input from peers across academia.

The Head of Research of Google Australia, Dr Grace Chung, visited UQ on 5 June. Prof Marta Indulska presented an overview of CIRES and research into Responsible AI, while Dr Hassan Khosravi spoke on Education and AI. Research discussion revolved around AI, particularly in the space of AI for Health, and Language, Search and Recommendation.

CIRES Postdoctoral researcher, Dr Junliang Yu, and CIRES CIs, Dr Rocky Chen and A/Prof Hongzhi Yin, presented a tutorial on Self-Supervised Learning for Recommendation: Foundations, Methods and Prospects at 28^th International Conference on Database Systems for Advanced Applications (DASFAA 2023), April 17-20, 2023, Tianjin, China.

Brief outline of the tutorial:

Recommender systems have become a necessity in this Internet era to offer personalization. However, in contrast to the increasing ease of model building and deployment, the lack of user behavioral data still remains a major pain point for modern recommender systems that constantly compromises recommendation performance. Recently, self-supervised learning (SSL), which can enable training on massive unlabeled data with automatic data annotation, has achieved tremendous success in many fields and been applied to an ever-expanding range of applications including recommendation. Many recent studies have demonstrated that all kinds of recommendation models can be significantly improved through learning with well-designed self-supervised tasks and data augmentations. In this tutorial, we will provide a panorama of the research efforts on self-supervised recommendation. Specifically, the content includes: (1) foundations and overview of self-supervised recommendation; (2) a comprehensive taxonomy of existing self-supervised recommendation methods; (3) how to apply SSL to various recommendation scenarios; (4) Challenges and future research directions.

Speakers Bio

Junliang Yu is a postdoctoral research fellow in the ARC Training Centre for Information Resilience (CIRES) at The University of Queensland. His research interests include recommender systems, social media analytics, deep learning on graphs, and self-supervised learning. He has 10+ publications on top-tier international venues such as KDD, WWW, ICDM, CIKM, AAAI, SIGIR, VLDBJ, and TKDE. He has been actively providing professional services to many toptier conferences/journals such as AAAI, CIKM, IJCAI, etc. He has rich lecture experience and tutored one relevant course of social media analytics, and also has made oral presentations on multiple top-tier conferences.

Dr. Tong Chen is a lecturer with the Data Science Discipline at The University of Queensland. He received his PhD degree in Computer Science from The University of Queensland in 2020. Dr. Chen’s research interests include data mining, machine learning, business intelligence, recommender systems, and predictive analytics. He has 60+ publications on top-tier international venues such as KDD, SIGIR, ICDE, AAAI, IJCAI, ICDM, WWW, TKDE, IJCAI, TOIS, and CIKM. He has been actively providing professional services to over 20 world-leading international conferences/journals in the fields of data mining, information retrieval and AI. Dr. Chen has ample track records in lecturing, witnessed by his course design and delivery experience in business analytics, full-course teaching experience in social media analytics and database systems, as well as invited talks on cutting-edge recommender systems at the WWW’22 Tutorial, ICDM’20 NeuRec Workshop, Beihang University, and Zhejiang University.

Dr. Hongzhi Yin works as ARC Future Fellow and associate professor with The University of Queensland, Australia. He received his doctoral degree from Peking University in July 2014. His current main research interests include recommender systems, graph embedding and mining, chatbots, social media analytics and mining, edge machine learning, trustworthy machine learning, decentralized and federated learning, and smart healthcare. He has published 220+ papers with Hindex 52, including 22 most highly cited publications in Top 1% (CNCI) venues such as KDD, SIGIR, WWW, WSDM, SIGMOD, VLDB, ICDE, AAAI, TKDE,etc. He has won 6 Best Paper Awards such as Best Paper Award at ICDE 2019, Best Student Paper Award at DASFAA 2020, and Best Paper Award Nomination at ICDM 2018. Dr. Yin has rich lecture experience and taught 5 relevant courses such as information retrieval and web search, data mining, social media analytics, and responsible data science. He was nominated as Most Effective Teacher of EAIT Faculty in The University of Queensland for 2020, 2021 and 2022. He has delivered 12 keynotes, invited talks and tutorials at the top international conferences such as tutorials at WWW 2017, KDD 2017 and WWW 2022.