Yong Ge

Yong Ge serves as Associate Professor of Management Information Systems at the Eller College of Management, University of Arizona, holding the Eller Fellow designation since joining in 2016 after prior faculty positions at the University of North Carolina at Charlotte. His academic foundation includes a PhD in Management Science and Information Systems from Rutgers University (2013).

His research program centers on business analytics, data mining, machine learning, and recommender systems, with emphasis on developing scalable algorithms for real-world business applications. Ge's work bridges theoretical advances in data science with practical implementations across transportation, tourism, mobile ecosystems, and social networks, demonstrating consistent innovation from 2011 through 2021.

Publication trends reveal evolving focus from foundational customer analytics (2011) to spatial data mining (2015-2019) and cutting-edge quantization techniques (2021), consistently targeting high-impact journals like IEEE TKDE and MIS Quarterly. His work demonstrates strong interdisciplinary connections between information systems, computer science, and business applications.

Key honors include:

• NSF CAREER Award (2019) for foundational research in data mining
• Dean's Research Award (Eller College, 2020)
• Ralph E. Powe Junior Faculty Enhancement Award (2015)
• IEEE ICDM Best Research Paper (2011)
• Excellence in Academic Research (Rutgers, 2013)

Ge secures significant research funding through competitive grants including the NSF CAREER award, supporting his work on algorithmic foundations for business intelligence. His teaching portfolio includes graduate courses in business intelligence (MIS 587) and design science research methodologies (MIS 611A), reflecting his commitment to developing next-generation analytics professionals.

No dedicated research laboratory or named research team is specified in available materials, though his collaborative publications indicate active engagement with cross-institutional research groups in data mining and recommender systems.