Associate Editor: Xiao Fang

University of Delaware

Term: January 1, 2017 – December 31, 2021

I am interested in designing novel and rigorous computational data science methods to solve critical and challenging problems in business and society. My research is usually motivated by real world business or societal problems and starts with a formal problem definition that is generalized from these problems. I strive to make methodological contributions in my research by developing novel machine or deep learning methods or algorithms to solve these problems innovatively and effectively. My recent research focuses on problem domains including Fintech, social network analytics, and healthcare analytics. I am also interested in studying fundamental data science problems: when and how to refresh knowledge discovered from evolving data sources, how to build machine learning models that can be learned from incomplete data, as well as trustworthy machine learning (e.g., explainability, fairness, and privacy). Methodologically, I usually use algorithm development, machine and deep learning, and mathematical modeling in my research.

In generally I am comfortable handling design science papers. I have a strong preference for design science research that makes clear methodological contributions in solving business or societal problems. I have experience handling papers using mixed methods such as machine learning and econometrics. But I have less background to evaluate behavioral research.

Email: xfang@udel.edu