Professor Hui Xiong
Dr. Hui Xiong is currently a Full Professor at the Rutgers, the State University of New Jersey. He also served as the Smart City Chief Scientist and the Deputy Dean of Baidu Research Institute in charge of several research labs (while on leave from Rutgers University). He received the Ph.D. degree from the University of Minnesota (UMN), USA. He is a co-Editor-in-Chief of Encyclopedia of GIS, an Associate Editor of IEEE Transactions on Big Data (TBD), ACM Transactions on Knowledge Discovery from Data (TKDD), and ACM Transactions on Management Information Systems (TMIS). Dr. Xiong has served as chair/co-chair for many international conferences in data mining, including a Program Co-Chair (2013) and a General Co-Chair (2015) for the IEEE International Conference on Data Mining (ICDM), and a Program Co-Chair of the Research Track (2018) and the Industry Track (2012) for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). Dr. Xiong’s research has generated substantive impact beyond academia. He is an ACM distinguished scientist and has been honored by the 2018 Ram Charan Management Practice Award as the Grand Prix winner from the Harvard Business Review, the 2017 IEEE ICDM Outstanding Service Award, the ICDM-2011 Best Research Paper Award， and the AAAI-2021 Best Paper Award. In 2020, he was named as an AAAS Fellow and an IEEE Fellow.
The big data trend has made its way to human resource management. Indeed, the availability of large-scale human resource (HR) data provide unparalleled opportunities for business leaders to understand talent behaviors and generate useful talent knowledge, which in turn deliver intelligence for real-time decision making and effective people management at work. In this talk, we introduce the powerful set of innovative Artificial Intelligence (AI) techniques developed for intelligent human resource management, such as recruiting, performance evaluation, talent retention, talent development, job matching, team management, leadership development, and organization culture analysis. In addition, we will also demonstrate how the results of talent analytics can be used for other business applications, such as market trend analysis and financial investment.