Yuan Yuan

Yuan Yuan

Ph.D. Candidate in Marketing
Tepper School of Business, Carnegie Mellon University

About

Hi, I'm Yuan — glad to have you here. I'm a Ph.D. candidate in Marketing at the Tepper School of Business, Carnegie Mellon University. My research sits at the intersection of machine learning and economics, spanning digital marketing, technology and innovation, causal inference, structural models applied to unstructured data, and interpretable ML.

Before CMU, I earned my bachelor's degree in Applied Math and CS at UC Berkeley and completed a pre-doc research fellowship in Economics at Stanford. Off the clock, my interests include skiing, the accordion, and ultimate frisbee.

Digital MarketingCausal InferenceML × EconomicsTechnology & InnovationInterpretable ML

Currently — developing structural and machine-learning methods for measuring fairness and disparities in online marketplaces.

Research

Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces
with Susan Athey, Dean Karlan & Emil Palikot · 2025
Major Revision · Management ScienceRead paper ↗
Gender and Racial Price Disparities in the NFT Marketplace
with Xiao Liu, Shunyuan Zhang & Kannan Srinivasan · 2024
Published · IJRMRead paper ↗
Clean Up Your Act: Impact of Self-Disclosure Regulation on Live Streaming
with Nikhil Malik, Wen Wang & Kannan Srinivasan · 2024
Work in progress

Teaching

Instructor of Record · Marketing I · Undergraduate, TepperSummer 2026

Faculty teaching rating: 5.0 / 5.0 (department average: 4.64; university average: 4.61).

Marketing I introduces undergraduates to how firms create and capture customer value. The course runs on discussion and case analysis, with an emphasis on collaboration, analytical thinking, and public speaking.

The capstone project matches OpenAI against Anthropic, two firms that share a market but pursue opposite paths to growth: one through consumer reach and the other through enterprise trust. Student teams examine each strategy from several angles, weighing tradeoffs in positioning, pricing, and monetization and testing where each approach holds up or breaks down. The open ended format asks teams to build original arguments and defend them with conviction.

Education & Awards

Education

Ph.D., Marketing · Carnegie Mellon, Tepper2021–
M.S., Marketing · Carnegie Mellon, Tepper2024
Pre-doctoral Fellow, Economics · Stanford University2019–21
B.A., Applied Math & CS · UC Berkeley2018

Awards

Student Fellow at the AMA-Sheth Foundation Doctoral Consortium · Fordham University 2026
Gerard Tellis Award for Excellence in Research · Artificial Intelligence in Management (AIM) Conference, USC Marshall2024
William Larimer Mellon Fellowship · Carnegie Mellon2021–
Dean's Research Fund · Tepper School of Business2024

Selected Talks

AMA-Sheth Foundation Doctoral Consortium · Fordham University 2026
Conference on Artificial Intelligence, Machine Learning, and Business Analytics · Columbia University 2025
INFORMS Annual Meeting · Atlanta 2025
Artificial Intelligence in Management (AIM) Conference · University of Southern California 2024
INFORMS Marketing Science · Miami 2023
Conference on Digital Experimentation (CODE) · Boston 2023
Conference on Artificial Intelligence, Machine Learning, and Business Analytics · Temple University 2023
Experimentation and Causal Inference in the Tech Sector Workshop · Stanford University 2023
International Crypto-Marketing · Columbia University 2022
Berkeley Artificial Intelligence Research Workshop · UC Berkeley 2019
Berkeley Artificial Intelligence Research Workshop · UC Berkeley 2018

Contact

It's always a pleasure to meet fellow curious minds. I'd be glad to hear from you.

yuany3@andrew.cmu.edu

Tepper School of Business · Carnegie Mellon University · Pittsburgh, PA