Keynote Speaker
Keynote Speaker
Prof. Ji Zhang
University of Southern Queensland, Australia
Brief biography:Professor Ji Zhang is a full Professor of Computer Science at the University of Southern Queensland (UniSQ), Australia. He is an IET Fellow, BCS Fellow, RSA Fellow, IEEE Senior Member, Australian Endeavour Fellow, Queensland International Fellow (Australia), and Izaak Walton Killam Scholar (Canada). He has extensive working experience in Singapore, Canada, Australia, the USA, and China over the past twenty years. He has published over 290 papers in major peer-reviewed international journals and conferences, including TKDE, TKDD, TIST, AAAI, IJCAI, VLDB, CIKM, SIGKDD, ICDE, ICDM, and WWW. Professor Zhang has received three best paper awards in WWW workshop 2021, DIKW 2021, and WISE 2019, as well as the student travel award of ICDM 2006. Professor Zhang has previously held visiting professor positions at the Agency for Science, Technology and Research (A*STAR), Singapore (2022), Michigan State University, USA (2010), and Nanyang Technological University (NTU), Singapore (2011).
Research Area:Prof. Ji Zhang's main researchinterests include big data analytics, knowledge discovery and data mining (KDD), and computational intelligence.
Prof.Changqing Zhang
University of Tianjin，China
Brief biography:Changqing Zhang is a professor and doctoral supervisor in the Department of Intelligence and Computing at Tianjin University. He is a top young talent under the National “Ten Thousand Talents Plan”. From 2017 to 2018, he served as a researcher at the University of North Carolina at Chapel Hill. He has published more than 100 papers in journals and international conferences such as IEEE TPAMI/IJCV/NeurIPS/ICLR, 20 papers have been cited more than 100 times, 5 papers have been selected as CVPR/NeurIPS oral reports or highlight papers, and 7 papers have been selected as ESI highly cited papers. Two papers were selected as ESI Hot papers. Google Scholar cited more than 6,000 times. The research results won the first prize in the Natural Science Award of the Chinese Image and Graphics Society, ICME Best Paper, and other awards. He was selected for the list of global high-potential Chinese young AI scholars released by Baidu and the list of the world's top 2% scientists released by Stanford University. Invited to review papers for many international high-level journals and conferences such as IEEE TPAMI, IJCV, CVPR, ICCV, ECCV, NeurIPS, ICML, and ICLR. Hosted and participated in a number of National Natural Science Foundation projects/key projects and national key research and development plan projects.
Research Area:Prof.Changqing Zhang 'smain research directions are machine learning, computer vision, and intelligent medical care.
Speech title:Reliable multimodal machine learning
Prof. Xiuyi Jia
Nanjing University of Science and Technology,China
Brief biography:Xiuyi Jia is a professor and doctoral supervisor at the School of Computer Science and Engineering/School of Artificial Intelligence, Nanjing University of Science and Technology. He serves as a member of the Special Committee on Granular Computing and Knowledge Discovery and the Special Committee on Machine Learning of the Chinese Society for Artificial Intelligence, an executive member of the Special Committee on Artificial Intelligence and Pattern Recognition of the Computer Society, a distinguished member of CCF, a senior member of CAAI, and a member of IEEE. The main research directions include machine learning, computer vision, etc. Published more than 100 academic papers in journals and conferences such as TPAMI, TKDE, TNNLS, NeurIPS, CVPR, ICCV, AAAI, etc., 3 papers were selected as ESI 1% highly cited papers, hosted 3 National Natural Science Foundation projects, and participated in key research and development, Joint Fund key projects and many other national and provincial projects.
Research Area:Prof.Xiuyi Jia'smain research directions are machine learning, data mining, pattern recognition, natural language processing, etc.
Speech title:Label Distribution Learning from the Perspective of Label Ranking
