Prof. Leon Shyue-Liang Wang
Speech Title: (To be Announced)
Leon Shyue-Liang Wang, currently distinguished professor and President of National University of Kaohsiung, received his Ph.D. from State University of New York at Stony Brook in 1984. From 1984 to 1994, he joined the University of New Haven and New York Institute of Technology as assistant/associate professor. From 1994 to 2002, he joined I-Shou University in Taiwan and served as director of computing center, chairman of information management department, and director of library. From 2003 to 2007, he rejoined NYIT. From 2009 to 2016, he was professor, chairman, Dean of College of Management, and vice president at National University of Kaohsiung, Taiwan. From August 2016, he started to serve as President of the University. He is the recipient of the 2011-2014, 2014-2017 national flexible wage awards from Ministry of Education in Taiwan, and a Fellow of Institute of Engineering and Technology in UK. He received 2016 outstanding leadership award from Chinese American Academic and Professional Society, New York, USA and a member of Phi Tau Phi scholar honor society. He is Editor-In-Chief of the International Journal of Information Privacy, Security and Integrity and was a former president of the Taiwanese Association of Social Networks. He has published over 230 papers in the areas of data mining, privacy preservation, soft computing, and served PC member and session chair of over 160 international conferences.
Prof. Cheng-Te Li
National Cheng-Kung University, Taiwan
How Powerful are Graph Neural Networks on Social Data Science?
Social networks depict how users connect and interact with one another, and enable crucial predictive tasks, including node classification (NC), link prediction (LP), and community detection (CD). With the blooming and advances of deep learning, novel Graph Neural Network (GNNs) models, which learn the representations of nodes, are invented and widely applied on social networks. How powerful are GNNs on social data science? In this talk, I will utilize our recent research outcomes to exhibit what, where, and how GNNs can benefit a variety of tasks on social networks and social media. First, we will demonstrate that semantics-preserving GNNs are able to significantly boost the performance of typical NC, LP, and CD tasks. Second, we will show that GNNs can be applied to better model and exploit diverse relationships between various types of nodes in the realms of recommender systems and homeland security. Third, through the tasks of fake news detection, cyberbullying detection, and air quality forecasting, we will further exhibit that GNNs are still powerful even when the graphs cannot be observed. At the end of this talk, I will discuss the future directions of GNN-empowered data science.
Dr. Cheng-Te Li is now an Associate Professor at Institute of Data Science and Department of Statistics, National Cheng Kung University (NCKU), Tainan, Taiwan. He received his Ph.D. degree (2013) from Graduate Institute of Networking and Multimedia, National Taiwan University. Before joining NCKU, he was an Assistant Research Fellow (2014-2016) at CITI, Academia Sinica. Dr. Li’s research targets at Machine Learning, Data Mining, Social Networks and Social Media Analysis, Recommender Systems, and Natural Language Processing. He has a number of papers published at top conferences, including KDD, WWW, ICDM, CIKM, SIGIR, IJCAI, ACL, EMNLP, and ACM-MM. Dr. Li’s academic recognitions include 2020 MOST FutureTech Award, 2019 K. T. Li Young Researcher Award, 2018 MOST Young Scholar Fellowship (The Columbus Program), and 2016 Exploration Research Award of Pan Wen Yuan Foundation. He leads Networked Artificial Intelligence Laboratory (NetAI Lab) at NCKU.