Hui Xiong Rutgers University | Professor, Former) Baidu | Head of Talent Intelligence Center
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). He has served regularly on the organization and program committees of numerous conferences, including as a Program Co-Chair of the Industrial and Government Track for the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), a Program Co-Chair for the IEEE 2013 International Conference on Data Mining (ICDM), a General Co-Chair for the IEEE 2015 International Conference on Data Mining (ICDM), and a Program Co-Chair of the Research Track for the 2018 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. He is an IEEE Fellow and an ACM Distinguished Scientist.
Artificial Intelligence in Human Resources
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 session, we will 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.