Events

Personalized pricing has become a key application of AI and machine learning, enabling firms to tailor decisions based on customer data and preferences. While personalization can significantly improve revenue and customer experience, it also raises important concerns regarding privacy and fairness. In this talk, I discuss two recent studies published in Management Science , both joint work with David Simchi-Levi and Yining Wang, that develop principled approaches for addressing these challenges.
The first study focuses on privacy-preserving personalized pricing. We develop a dynamic pricing framework based on differential privacy that protects sensitive customer information and purchase behavior while still enabling effective demand learning.
The second study examines fairness in personalized pricing. We propose a utility-based fairness framework requiring customers with similar underlying utility to receive similar prices. We characterize the structure of optimal fair pricing policies and further develop a rate-optimal contextual bandit algorithm with provable performance guarantees.
Together, these works illustrate how privacy and fairness considerations can be embedded directly into modern learning and optimization systems, providing a foundation for AI-driven decision-making that is both economically effective and socially responsible.

Xi Chen is the Andre Meyer Full Professor at NYU Stern School of Business and an affiliated faculty member of the Courant Institute of Mathematical Sciences and NYU’s Center for Data Science. He earned his Ph.D. in Computer Science from Carnegie Mellon University and completed a postdoctoral fellowship at UC Berkeley, advised by Michael I. Jordan.

Xi has held senior industry roles, including full-time science leadership at Amazon Ads (2021–2023), where he led forecasting, pricing, recommendation, and delivery systems for a multi-billion-dollar video advertising marketplace.

He is a Fellow of the IMS (Institute of Mathematical Statistics) and ASA (American Statistics Association), has published 120+ papers across AI, statistics, and operations research, and is a Forbes 30 Under 30 (Science) and Poets & Quants 40 Under 40 honoree