2019 年 11 月 22 日（周五）下午，爱荷华大学赵慷老师，应邀在北京师范大学系统科学学院做报告，集智俱乐部将对本次讲座活动进行全程直播。
Mining Collaboration Networks to Understand Individual Performance—From Research Co-authorship to Business Partnership
Dr. Kang ZhaoUniversityof Iowa赵慷，爱荷华大学Dr. Kang Zhao is an Associate Professor of Business Analytics, with a joint appointment in Informatics, at the University of Iowa. He leads the Data and Network Analytics Research Group. His current research focuses on data science and business intelligence, especially the mining, modeling, and simulation of social media, online communities, and social/business networks. He has published more than 30 journal papers and his research has been featured in public media from more than 25 countries, such as Washington Post, USA Today, Forbes, Yahoo News, New York Public Radio, BBC and Agence France-Presse. He served as the Chair for INFORMS Artificial Intelligence Section (2014-2016). He has been an associate editor for ICIS, a guest editor for IEEE Intelligent Systems, and an NSF panelist. He is currently an editor for Journal of the Association for Information Science and Technology, and a PC co-chair of the 2019 INFORMS Data Science Workshop. He has been a reviewer for 30+ journals and a PC member for more than 10 conferences/workshops. He was the recipient of Tippie College of Business Early Career Research Awards and the best paper award of the 2017 INFORMS Data Science Workshop.
In our inter-connected world, collaboration becomes increasingly important for individuals and organizations to mobilize external resources and achieve goals that are beyond their own capacities. This talk will introduce two streams of my research on mining the data of collaboration networks of individual people and organizations to better understand their performance. I will first talk about my research on how researchers’ job placement and research impact can be analyzed and predicted by their collaboration networks. Then I will cover projects on modeling disruption propagation in supply chain networks and improving firms’ performance against such disruptions.