报告题目：
Privacy Management in Data-Driven Operations
报 告 人：加拿大女王抢庄牛牛 史密斯商抢庄牛牛 雷彦哲副教授
① 2025年11月25日（周二） 10:30-12:00
② 2025年11月27日（周四） 10:30-12:00
报告地点：腾讯会议 924-4026-5622
报告摘要：
This workshop examines the intersection of privacy management and data-driven decision making through the lens of differential privacy. We begin by motivating privacy as a first-class operational constraint in many business context, then develop the core theory of differential privacy: formal definitions, mechanisms, composition, and optimal noise design. Building on this foundation, we study how privacy requirements reshape statistical learning and online decision-making, with a focus on private ERM and differentially private multi-armed bandits and contextual bandits. We then connect these tools to recent operations management research on privacy-preserving personalized pricing, feature-based newsvendor learning, and optimal data acquisition with privacy-aware agents.
报告人简介：
Yanzhe Lei is an Associate Professor of Management Analytics and a Distinguished Research Fellow at the Smith School of Business, Queen's University. His research focuses on developing data-driven frameworks that enable companies to make dynamic decisions under uncertainty. He also investigates how data-driven analytics can be aligned with societal values, particularly in the areas of privacy, fairness, and sustainability. Yanzhe's research has been published in top-tier peer-reviewed academic journals and is supported by various funding agencies. His achievements have been recognized through multiple awards, including the New Researcher Achievement Award. He has collaborated with leading global tech companies such as Alibaba, Instacart, Flipkart, Staples, and Oracle Retail Labs, where he has contributed to developing real-time, large-scale analytics solutions.
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