學院消息

Time：2024.06.05(Wed.) 15:30-17:20

Venue：工程三館EC115教室

Speaker：李政德教授 成功大學資工系

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Title
Bridging Trust: Combating Fakes and Frauds with Robust Graph Learning
Abstract
In the digital era, where misinformation and fraud proliferate, robust graph learning emerges as a critical tool for restoring trust. This talk delves into the advances graph learning for counteracting the complexities of fake news, rumors, and fraudulent activities across various domains. Leveraging the robust graph neural networks learned from transactions, text, and tabular datasets, this talk will show how innovations in generative methods, such as data augmentations and synthesis, and self-supervised learning, such as contrastive and curriculum learning, are reshaping the landscape of trustworthiness in digital and physical marketplaces. These advanced graph models not only enhance the detection and mitigation of deceitful practices but also pave the way for more transparent, reliable information exchange. The presentation will highlight graph learning’s effectiveness in building robust ecosystems immune to falsehoods, focusing on applications in rumor and customs fraud detection, AI-driven food safety, and social media privacy. In doing so, it will present a strategy for employing powerful graph models to navigate and neutralize the challenges brought on by disinformation and deceit, showcasing a committed pathway toward upholding truth in our interconnected society.
Bio
Dr. Cheng-Te Li is currently Full Professor at the Department of Computer Science and Information Engineering, National Cheng Kung University (NCKU) in Tainan, Taiwan. He earned his Ph.D. degree in 2013 from the Graduate Institute of Networking and Multimedia at National Taiwan University. Prior to joining NCKU, Dr. Li served as an Assistant Research Fellow at CITI, Academia Sinica, from 2014 to 2016. Focusing on Machine Learning and Data Mining, Dr. Li's research explores their applications in Social Networks, Social Media, Recommender Systems, and Natural Language Processing. His work has been featured at premier conferences such as KDD, TheWebConf (WWW), ICDM, CIKM, SIGIR, IJCAI, ACL, EMNLP, and NAACL. Recently, his group has presented lecture-style tutorials on Graph Neural Networks at top conferences, including WWW, IEEE ICDE, and ACML. Dr. Li's academic achievements have been widely recognized, earning him important awards including the CIEE Outstanding Youth Electrical Engineer Award (2023), Y. Z. Hsu Scientific Paper Award (2022), FAOS Young Scholars' Creativity Award (2021), MOST Future Tech Awards (2023, 2021, 2020), TAAI Domestic Track Best Paper Award (2020), K. T. Li Young Researcher Award (2019), and MOST Young Scholar Fellowship (2018). Dr. Li leads the Networked Artificial Intelligence Laboratory (NetAI Lab) at NCKU.