My research lies at the intersection of marketing and artificial intelligence (AI), centered on the elicitation and modeling of consumer preferences and choices in an increasingly complex and data-rich world. By bridging core marketing problems with recent advances in AI, my research pushes the boundaries of what we can model and predict about consumer preferences. It aims to deepen our theoretical understanding of how consumers make decisions and to provide novel, actionable, proof-of-concept solutions for firms to adopt. In particular, my research focuses on three areas. First, I investigate how to model consumer preferences with high-dimensional unstructured data, such as images and video. This research stream is becoming increasingly important as consumer behavior data becomes exponentially richer in the modern world. Second, I expand models of heterogeneous consumer preferences. Finally, I develop new generative AI-based methods for marketing research and behavioral research. A common theme in my work is uncovering new knowledge that was previously out of reach due to data or modeling limitations, and reexamining fundamental questions under more realistic assumptions about nuanced consumer decision-making.
MKTG 4300 - Pricing and Channels of Distribution
Primary Instructor
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Fall 2018 / Fall 2019
Offered regularly to examine pricing and channel management, the two key components of companys' marketing strategies. Help students to understand the common types of pricing and channel strategies, the rationales behind these strategies. Train students to think analytically in order to apply these strategies. Required for marketing majors.
MSBC 5190 - Modern Artificial Intelligence: Introduction to AI for Business
Primary Instructor
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Spring 2022 / Spring 2023 / Spring 2024
Provides students with a comprehensive introduction of recent developments in AI by covering fundamental AI concepts and practical applications of these concepts in business. Will review major advances in AI subfields of Deep Learning, Reinforcement Learning, Natural Language Processing, Computer Vision, Recommender Systems, Robotics, and others. Students will learn how to apply AI-based methods to solving practical business problems, acquiring acquire knowledge and hands-on experience of modern AI tools, including the Deep Learning framework Tensorflow. Recommended prerequisite: experience in Python and basic probability/statistics.
