Algorithm Design in Online Marketplaces

In this talk, Dr. Yiding Feng will describe his research motivated by online marketplaces. The first half of the talk will depart from the classic online matching literature, and see how the inefficiency can be reduced by the batching strategy. The second half of the talk will depart from the classic mechanism design literature, and study how to analyze common auctions and design new ones in the digital advertising market.

Abstract

Thanks to the rapid growth of modern technology, online marketplaces have become an important component of today’s economy. In many online marketplaces, the platforms are usually equipped with new algorithmic powers but also confront additional technical challenges. The emergence of such power and challenge has led to new interest in computer science research and enriched the field with exciting problems. In this talk, I will describe my research motivated by online marketplaces. In the first half of the talk, we will depart from the classic online matching literature, and see how the inefficiency can be reduced by the batching strategy. In the second half of the talk, we will depart from the classic mechanism design literature, and study how to analyze common auctions and design new ones in the digital advertising market.

Biography

Yiding Feng is a postdoctoral researcher at Microsoft Research New England, where he is a member of the Economics and Computation group. He previously received his Ph.D. from the Department of Computer Science, Northwestern University in 2021 where his advisor was Jason D. Hartline. His research focuses on the design and analysis of algorithms in online marketplaces, particularly ones dealing with uncertainty and incentives.