Yicheng Song

Yicheng Song serves as an Assistant Professor in the Department of Information & Decision Sciences at the Carlson School of Management, University of Minnesota. His research develops personalization technologies using machine learning and structural economic models to enhance user satisfaction and business revenue through data-driven solutions.

His educational background includes:

• PhD in Business (2017), Questrom School of Business, Boston University
• PhD in Computer Science (2012), Institute of Computing Technology, Chinese Academy of Sciences
• BA in Computer Science (2006), Wuhan University

Dr. Song's research specializes in Personalized Recommender Systems, Digital Consumer Analytics, and Charity Analytics. He develops advanced machine learning frameworks to address information overload in digital platforms, with applications spanning e-commerce, customer journey optimization, and nonprofit donation systems. His work uniquely bridges technical innovation in AI with practical business impact through structural economic modeling.

Analysis of his publication trajectory (2011-2024) reveals consistent focus on personalization algorithms, evolving from multimedia geolocation to reinforcement learning for customer interventions. Recent work emphasizes explainable AI in donor matching systems and multi-category utility modeling, demonstrating increasing sophistication in handling complex consumer behavior data across digital and charitable contexts.

His research excellence has been recognized with prestigious awards:

• Best Paper Award at INFORMS CIST 2018
• Best Paper Award at WITS 2014
• Runner-up for Best Paper Award at INFORMS CIST 2023

While specific grant details and student advising records aren't publicly listed, his publications indicate active collaboration with researchers in marketing, operations, and computer science. The absence of laboratory information suggests integration within the Carlson School's existing research infrastructure rather than standalone facilities.