Graph Neural Network Training: From Data Management Perspective
暫譯: 圖神經網絡訓練：從數據管理的角度

Yanyan Shen is an associate professor at Shanghai Jiao Tong University, specializing in machine learning and data management systems. Her research focuses on developing scalable and efficient algorithms for large-scale data processing, with a strong emphasis on practical applications and system-level optimizations. She has authored numerous papers in leading journals and conferences, contributing significantly to the intersection of AI and data management.

Lei Chen is a Chair Professor in the Data Science and Analytics Thrust at HKUST (GZ), a Fellow of IEEE, and a Distinguished Member of ACM. His research spans diverse areas, including data-driven AI, knowledge graphs, blockchain, data privacy, crowdsourcing, spatial and temporal databases, and query optimization for large graphs and probabilistic databases. Prof. Chen has received numerous accolades, such as the SIGMOD Test-of-Time Award (2015), the Best Research Paper Award at VLDB (2022), and the Excellent Demonstration Award at VLDB (2014). He served as the PC Co-Chair for VLDB 2019 and is currently the Editor-in-Chief of IEEE Transactions on Data and Knowledge Engineering, as well as an executive member of the VLDB Endowment.

閻妍是上海交通大學的副教授，專注於機器學習和數據管理系統。她的研究重點在於開發可擴展且高效的大規模數據處理算法，並強調實際應用和系統層級的優化。她在領先的期刊和會議上發表了多篇論文，對人工智慧（AI）和數據管理的交集做出了重要貢獻。

陳雷是香港科技大學（廣州）的數據科學與分析領域的講座教授，IEEE 會士，以及ACM的傑出會員。他的研究涵蓋多個領域，包括數據驅動的人工智慧、知識圖譜、區塊鏈、數據隱私、群眾外包、空間和時間數據庫，以及大圖和概率數據庫的查詢優化。陳教授獲得了多項榮譽，包括SIGMOD 時間考驗獎（2015年）、VLDB最佳研究論文獎（2022年）和VLDB優秀展示獎（2014年）。他曾擔任VLDB 2019的程序委員會共同主席，目前是IEEE Transactions on Data and Knowledge Engineering的主編，以及VLDB基金會的執行委員會成員。