报 告 人：Xiao Fang 教授
The US National Science Foundation (NSF) classifies AI research into two categories: fundamental AI research and use-inspired AI research. The former aims to develop theory and methods that are independent of any particular domain of application whereas the latter seeks new methods and understanding in AI by situating the research in a domain of application to simultaneously inform progress in AI and solve particular use cases. NSF emphasizes that use-inspired AI is not applied AI because it develops novel AI algorithms and methods inspired by important business, societal, scientific, and engineering problems. Positioned at the intersection of technology and business, researchers in the field of Information Systems (IS) are well-suited to carry out use-inspired AI research. In particular, computational design science research, which is concerned with solving business and societal problems by developing novel computational algorithms and methods, is use-inspired AI research in the IS field. In this talk, I will discuss what computational design science research is and why it is unique (especially in comparison to machine learning research in the field of Computer Science). I will also illustrate computational design science research with an example.
Xiao Fang is Professor of MIS at Lerner College of Business, University of Delaware. He also holds appointments at Department of Computer Science as well as Department of Electrical and Computer Engineering, University of Delaware. His current research focuses on financial technology, social network analytics and health care analytics with methods and tools drawn from reference disciplines including computer science (e.g., machine learning) and management science (e.g., optimization). He has published in business journals including MS, OR, MISQ and ISR as well as computer science outlets such as ACM TOIS and IEEE TKDE. Professor Fang co-founded INFORMS Workshop on Data Science in 2017. He was an associate editor for MIS Quarterly and currently serves on the editorial board of Information Systems Research, INFORMS Journal on Data Science, and Service Science (INFORMS)。