Tongliang Liu is the Director of Sydney AI Centre at the University of Sydney. He is also a visiting professor at the University of Science and Technology of China, China; an affiliated professor with the Mohamed bin Zayed University of Artificial Intelligence, UAE; a visiting scientist with RIKEN AIP, Japan. He is broadly interested in the fields of trustworthy machine learning and its interdisciplinary applications, with a particular emphasis on learning with noisy labels, adversarial learning, causal representation learning, transfer learning, unsupervised learning, and statistical deep learning theory. He has authored and co-authored more than 200 research articles including ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV, AAAI, IJCAI, TPAMI, and JMLR. He is/was a senior meta reviewer for many conferences, such as NeurIPS, ICLR, AAAI, and IJCAI. He is a co-Editor-in-Chief for Neural Networks, an Associate Editor of IEEE TPAMI, IEEE TIP, TMLR, and ACM Computing Surveys, and is on the Editorial Boards of JMLR and MLJ. He is a recipient of CORE Award for Outstanding Research Contribution in 2024, the IEEE AI’s 10 to Watch Award in 2022, the Future Fellowship Award from Australian Research Council (ARC) in 2022, the Top-40 Early Achievers by The Australian in 2020, and the Discovery Early Career Researcher Award (DECRA) from ARC in 2018.

Hongzhi Yin works as an ARC Future Fellow and Professor and director of the Responsible Big Data Intelligence Lab (RBDI) at The University of Queensland, Australia. He has made notable contributions to predictive analytics, recommendation systems, graph learning, social media analytics, and decentralized and edge intelligence. He has received numerous awards and recognition for his research achievements. He has been named to IEEE Computer Society’s AI’s 10 to Watch 2022 and Field Leader of Data Mining & Analysis in The Australian's Research 2020 magazine. In addition, he has received the prestigious 2023 Young Tall Poppy Science Awards, the Australian Research Council Future Fellowship 2021, the Discovery Early Career Researcher Award 2016, UQ Foundation Research Excellence Award 2019, the Rising Star of Science Award (2022-2024), and 2024 Computer Science in Australia Leader Award, AI 2000 Most Influential Scholar Honorable Mention in Data Mining (2022-2024). His research has won 8 international and national Best Paper Awards, including Best Paper Award - Honorable Mention at WSDM 2023, Best Paper Award at ICDE 2019, Best Student Paper Award at DASFAA 2020, Best Paper Award Nomination at ICDM 2018, ACM Computing Reviews' 21 Annual Best of Computing Notable Books and Articles, Best Paper Award at ADC 2018 and 2016. His Ph.D. thesis won Peking University Outstanding Ph.D. Dissertation Award 2014 and CCF Outstanding Ph.D. Dissertation Award (Nomination) 2014. He has ten conference papers recognized as the Most Influential Papers in Paper Digest, including KDD 2021 and 2013, AAAI 2021, SIGIR 2022, WWW 2023 and 2021, CIKM 2021, 2019, 2016, and 2015. He has published over 300 papers with an H-index of 78, including 210+ CCF A/CORE A* and 80+ CCF B/CORE A, such as KDD, SIGIR, WWW, WSDM, SIGMOD, VLDB, ICDE, AAAI, IJCAI, ACM Multimedia, ECCV, IEEE TKDE, TNNL, VLDB Journal, and ACM TOIS. He has been an AC/SPC/PC member for many top conferences, such as WWW, KDD, AAAI, IJCAI, ICML, ICLR, NeurIPS, SIGIR, WSDM, VLDB, ICDE, ICDM, and CIKM.

Sen Wang is an Associate Professor and leading researcher in the field of data science at the School of Electrical Engineering and Computer Science (EECS) of the University of Queensland. Dr Wang focuses on ML research topics, including multimodal learning, knowledge distillation, multi-agent reinforcement learning, efficient neural networks for edge devices, and temporal knowledge graph mining. He also extends his research excellence in AI/ML to many multidisciplinary fields, such as eHealth and smart agriculture. His contributions to medical data analysis include real-time series analysis for ICU decision support and disease progression modelling. A/Prof Wang has a robust publication record with over 100 academic papers in top-tier journals and conferences, such as CVPR, WACV, ICCV, NeurIPS, ICLR, ACM MM, AAAI, ICDM, KDD, IJCAI, TMM, TKDE, TKDD, TNNLS, TCYB, etc. As an excellent EMCR, he has led and participated in many competitive fundings as one of the essential CIs (ARC DECRA, 2 * ARC DP, 2 * ARC ITTC). Also, he serves the community as an invited pc/senior pc/area chair for many top-tired conferences and journals.

Jordan Pitt is a descendant of the Birri Gubba people, Associate Dean of Indigenous Strategy and Services, and Applied Mathematician at the University of Sydney. He completed his PhD at the Australian National University in 2019, developing methods to model the inundation caused by tsunamis and storm surges. His current research, begun as a post-doctoral researcher at the University of Adelaide, focuses on modeling the interaction between ocean waves and sea ice, which forms as the ocean’s surface freezes. This interaction influences the annual growth and melt of sea ice, which is a key indicator and driver of the Earth’s climate.

Dr. Weitong Chen is an ARC EC Industry Fellow and Senior Lecturer at the Australian Institute for Machine Learning (AIML) at the University of Adelaide, where he also serves as the Co-director of the Data Transpose Lab. He represents Early and Mid-Career Researchers (EMCR) at the University of Adelaide. Dr. Chen completed both his PhD and Master's degrees at the University of Queensland, Australia. His research includes over 40 peer-reviewed publications in top-tier journals and conferences such as ICLR, IJCAI, AAAI, WWW, ACML, CIKM, IEEE ICDM, SIAM DM, WWWJ, and TKDD. He has received numerous accolades, including the Early Career Research Excellence Award (UoA'23), Best Student Paper awards, a Best Paper award, the Premier's Award for Open Data, and the Microsoft Open Data Award. Dr. Chen's work focuses on machine learning, particularly its application in medical data analytics, fostering collaborations with universities, industry, governmental bodies, and professional organizations. His research has attracted over $1.4 million in grant funding, highlighting the impact and potential of his contributions.