Broadly speaking, Liu's research lies in the intersection of marketing and machine learning. She is interested in developing new methodologies and tailoring state-of-the-art machine learning and deep learning methods to marketing problems, in areas such as visual marketing, branding, product design and innovation, social media, and consumer choice modeling. In particular, her research focuses on the elicitation, measurement, and prediction of consumer preferences and choices. Her work takes the advantage of the efficiency and scalability of machine learning and are tailored specifically to marketing applications with a deep understanding of consumer behavior and economic theories.
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.
