Hongye Sun
Ph.D. Student
Department of Marketing, Ph.D. Program
Ph.D.
Hongye Sun is a Ph.D. candidate in Quantitative Marketing at the University of Utah, slated to graduate in 2026. With a strong foundation in econometrics and advanced analytics, Hongye’s research leverages cutting-edge methodologies including natural experiments, econometric modeling, and machine learning applications in marketing.
His work focuses on bridging sophisticated quantitative methods with real-world marketing challenges, particularly in the domains of salesforce optimization, deep learning applications in retail, and the integration of artificial intelligence in marketing strategies. Hongye’s current projects, in collaboration with major firms and leading scholars, investigate the impact of large language models on customer service efficiency, the optimization of salesforce performance through causal inference, and the application of deep learning in visual marketing analysis for large-scale retail operations.
Hongye has presented his research at the prestigious INFORMS Marketing Science Conference and is preparing submissions to top-tier marketing journals. His quantitative skills span a wide range of tools including Python, STATA, and R. With a M.Phil. ‘s degree from Nanjing University and a Bachelor’s from Jilin University, both in Marketing, Hongye combines rigorous academic training with a forward-looking approach to quantitative marketing research. His work promises to contribute significantly to both the theoretical understanding and practical application of data-driven marketing strategies.
Hongye’s personal website: https://hongyesun.org/.
University of Utah, USA
2021 – 2026 (expected)
Ph.D. in Marketing
Nanjing University, China
2017 – 2020
M.Phil. Marketing, GPA: 85/100, rank: 2/13
Jilin University, China
2013 – 2017
B.A. Marketing, GPA: 89/100, 3.87/4.0, top 5%
Hongye Sun is a PhD candidate specializing in Quantitative Marketing, whose research stands at the intersection of econometrics, computer science with marketing. His work is characterized by the application of advanced causal inference strategies, machine learning algorithms, and big data analytics to address complex marketing challenges.
His work also involves implementing large-scale field experiments to test and validate theoretical models, demonstrating a commitment to both methodological rigor and practical applicability. This approach has led to fruitful partnerships with diverse industries, ranging from major sales companies, giant retail chains, and e-commerce sellers to top car manufacturers, innovative product design firms, and leading training centers.
Having presented at the prestigious INFORMS Marketing Science Conference and with submissions in preparation for top-tier journals, Sun is poised to make significant contributions to the field of quantitative marketing. As he completes his PhD, Sun aims to continue advancing the discipline through computationally sophisticated research that bridges the gap between theoretical advancements and practical applications in today’s data-rich marketing landscape.
- (expected) Lecturer to Principles of Marketing, University of Utah (Spring 2025)
- Teaching Assistant to Principles of Marketing, University of Utah (Fall 2024)
- Teaching Assistant to Marketing Analysis, University of Utah (Fall 2023)
- Teaching Assistant to Marketing Research, University of Utah (Spring 2022, Spring 2023)
- Teaching Assistant to Marketing Research, Nanjing University (Mar.2018 – Jun.2019)
- Teaching Assistant to Information System Management, Nanjing University (Mar.2018 – Jun.2019)
- Outstanding Graduate, Nanjing University (2020)
- First-class Scholarship, Nanjing University (2017)
- First-class Scholarship, Jiling University (2015)
