Bio
Dr. Huan Liu is a Regents professor of computer science and engineering with the School of Computing and Augmented Intelligence in the Ira A. Fulton Schools of Engineering. At Arizona State University, he was recognized for excellence in teaching and research in Computer Science and Engineering and received the 2014 President's Award for Innovation. He is the recipient of the ACM SIGKDD 2022 Innovation Award. His research interests are in data mining, machine learning, feature selection, social computing, and artificial intelligence. He is a co-author of a text, Social Media Mining: An Introduction, Cambridge University Press. He is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction, and Chief Editor of Data Mining and Management in Frontiers in Big Data. He is a Fellow of ACM, AAAI, AAAS, and IEEE.

Bio
James Kwok is a Professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. He is an IEEE Fellow.

Prof Kwok received his B.Sc. degree in Electrical and Electronic Engineering from the University of Hong Kong and his Ph.D. degree in computer science from the Hong Kong University of Science and Technology. He then joined the Department of Computer Science, Hong Kong Baptist University as an Assistant Professor. He returned to the Hong Kong University of Science and Technology and is now a Professor in the Department of Computer Science and Engineering. He is serving / served as an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, Neural Networks, Neurocomputing, Artificial Intelligence Journal, International Journal of Data Science and Analytics, and an Action Editor of Machine Learning. He is also serving as Senior Area Chairs of major machine learning / AI conferences including NeurIPS, ICML, ICLR, IJCAI, and as Area Chairs of conferences including AAAI and ECML. He is on the IJCAI Board of Trustees. He is recognized as the Most Influential Scholar Award Honorable Mention for "outstanding and vibrant contributions to the field of AAAI/IJCAI between 2009 and 2019". Prof Kwok is the IJCAI-2025 Program Chair.

Bio
MYRA SPILIOPOULOU is Professor of Business Information Systems at the Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Germany. Her main research is on mining dynamic complex data. Her publications are on mining complex streams, mining evolving objects, adapting models to drift and building models that capture drift. She focusses on two application areas: (a) business, including opinion stream mining and adaptive recommenders, and (b) medical research, including epidemiological mining and learning from clinical studies. In the application domain of medical research, she works on modeling and predicting evolution of study participants with and without the target outcome. In the area of medical research, she is currently involved in the UNITI (2020-2022) EU Project on “Unification of treatments and Interventions for Tinnitus patients”.

Her research on topic monitoring, social network monitoring and analysis of complex dynamic data has been published in renowned international conferences and journals. She is regularly presenting tutorials on different aspects of complex data mining, and recently on medical mining. She is involved as (senior) reviewer in major conferences on data mining and knowledge discovery. In 2016 and 2019, she served as a PC Chair of the IEEE Int. Symposium on Computer-Based Medical Systems (CBMS). In 2021, she served as a Special Sessions Chair of the IEEE DSAA (Data Science And Analytics) conference and as ECML PKDD 2021 Awards Chair.

Bio
Vincent S. Tseng is currently a Chair Professor at Department of Computer Science in National Yang Ming Chiao Tung University (NYCU). He served as the founding director for Institute of Data Science and Engineering in NCTU during 2017-2020, chair for IEEE CIS Tainan Chapter during 2013-2015 and the president of Taiwanese Association for Artificial Intelligence during 2011-2012. He also acted as the director for Institute of Medical Informatics of National Cheng Kung University during 2008 and 2011. Dr. Tseng received his Ph.D. degree with major in computer science from National Chiao Tung University, Taiwan, in 1997. After that, he joined Computer Science Division of EECS Department in University of California at Berkeley as a postdoctoral research fellow during 1998-1999. He has a wide variety of research interests covering artificial intelligence, big data mining, machine learning, biomedical informatics, mobile and Web technologies, etc. He has published more than 400 research papers in referred journals and conferences as well as 15 patents (held and filed). By Google Scholar, his publications have been cited by more than 16,000 times with H-Index 68. He has been on the editorial board of a number of top journals including IEEE Transactions on Knowledge and Data Engineering, IEEE Journal of Biomedical and Health Informatics, ACM Transactions on Knowledge Discovery from Data, Knowledge Discovery and Data Mining, etc. He has also served as chairs/program committee members for a number of premier international conferences related to data mining and intelligent computing, including KDD, ICDM, SDM, PAKDD, ICDE, CIKM, IJCAI, etc. In recent years, he has also been serving to oversee the architect on Big Data/AI technologies and applications for the governmental and industrial units in Taiwan. Dr. Tseng has received numerous prestigious honors/awards, including World Top 2% Scientist (since 2022), PAKDD Distinguished Service Contribution Award (2024), Pan Wen Yuan Foundation Outstanding Research Award (2023), IICM Medal of Honor (2021), Outstanding Research Award (2019 & 2015) by Ministry of Science and Technology Taiwan, 2019 ACM Distinguished Scientist Member (for Outstanding Scientific Contributions to Computing), FutureTech Award (2023, 2022 & 2018), 2018 Outstanding I.T. Elite Award, and 2014 K. T. Li Breakthrough Award. He is also a Fellow of IEEE and Distinguished Member of ACM.

In terms of the technical achievements, Dr. Tseng has advanced the area of big data mining/machine learning and interdisciplinary applications substantially at both algorithmic and system levels, with solving broad classes of challenging problems and enabling emerging applications. He has made significant contributions to the data mining and intelligent computing community in advancing the area of utility patterns mining and biomedical informatics analytics by not only creating new research lines but also innovating valuable applications. Dr. Tseng has applied the developed data mining/machine learning techniques extensively to interdisciplinary applications, especially in the field of smart medicine. Over more than 20 years, he has led his research team to conduct many important and pioneering research/industrial projects. The developed techniques, including artificial intelligence, big data analytics, machine learning and text mining, have been integrated for early detection/prediction of various kinds of diseases from heterogeneous biomedical data sources like medical images, vital signs, medical notes and genomes. These techniques and platforms have been extensively applied in practical healthcare domains through collaborations with various medical centers/institution in Taiwan, resulting many pioneering publications and practical applications in biomedical and healthcare domains. The representative work includes the risk detection/prediction techniques for Diabetic Retinopathy (collaborated with Industrial Technology Research Institute), which delivered AUC higher than 98% (the highest in the world in this area). The resulted and extended technologies /systems have won a number of prestigious awards, including the National Innovation Award Taiwan (2020-2023), ITRI Yearly Paper Award (2020), and the world-renowned R&D100 Award (2022) and Edison Awards (2022).

Bio
Professor Lee is a Professor at School of Computing, Korea Advanced Institute of Science and Technology (KAIST) and leads the Data Mining Lab. Before that, he was a Postdoctoral Researcher at IBM Almaden Research Center and a Postdoc Research Associate at Department of Computer Science, University of Illinois at Urbana-Champaign. At IBM, he was one of the key contributors to IBM Smart Analytics Optimizer. At University of Illinois, he worked on spatio-temporal data mining with Prof. Jiawei Han. He earned his Ph.D degree under the supervision of Prof. Kyu-Young Whang from KAIST in 2005. His research interests encompass data-centric AI, deep learning-based big data analysis, mobility and stream data mining, and distributed deep learning. His lab has been selected as a SW Star Lab by the Ministry of Science and ICT in 2020.

Bio
Shirui Pan is a Professor and an ARC Future Fellow with the School of Information and Communication Technology, Griffith University, Australia. He is a Co-Director of TrustAGI Lab. Before joining Griffith in 2022, he was Senior Lecturer (Associate Professor) with the Faculty of Information Technology, Monash University. He received his Ph.D degree in computer science from University of Technology Sydney (UTS), Australia. He is a Senior Member of IEEE and ACM, and a Fellow of Queensland Academy of Arts and Sciences (FQA).

His research focuses on artificial intelligence and machine learning. His research was cited 40,438 times, H-Index: 76 (on April 08, 2025).

He has made contributions to advance graph machine learning methods for solving hard AI problems for real-life applications, including anomaly detection, graph analytics, and time series forecasting. He is recognized as one of the 2024 AI’s 10 to Watch and the recipient of the 2024 IEEE ICDM Tao Li Award. He has also been named among the AI 2000 AAAI/IJCAI Most Influential Scholars in Australia (2023, 2022), and one of the World’s Top 2% Scientists (2022, 2021). His research received the 2020 IEEE ICDM Best Student Paper Award (2020), and the 2024 IEEE CIS TNNLS Outstanding Paper Award. He has eight papers recognised as the Most Influential Papers in KDD (x1), IJCAI (x5), AAAI (x1), and CIKM (x1) (Feb 2022). He received a prestigious Future Fellowship (2022-2025), one of the most competitive grants from the Australian Research Council (ARC).