- Ph.D. Candidate in Marketing
- University of Washington, Foster School of Business
I am a Ph.D. candidate in quantitative marketing at the Foster School of Business, University of Washington. My research interests broadly encompass topics related to digital marketing, mobile advertising, personalization, and privacy. I examine these topics through two complementary lenses – (1) how can we utilize the recent advancements in machine learning to create value in digital marketplaces, and (2) how can we use theory-driven structural frameworks to study the marketing and economic implications of such developments.
In Summer 2020, I will be joining Cornell Tech and the SC Johnson School of Management at Cornell University as an Assistant Professor of Marketing.
Substantive areas: digital marketing, mobile advertising, targeting, personalization, privacy, online auctions.
Methods: policy evaluation, structural models, machine learning, reinforcement learning, mechanism design, causal inference.
Rafieian, Omid, and Yoganarasimhan, Hema, "Targeting and Privacy in Mobile Advertising."
Forthcoming at Marketing Science
Rafieian, Omid, and Yoganarasimhan, Hema, "How Does Variety of Previous Ads Influence Consumer’s Ad Response?"
Rafieian, Omid, "Optimizing User Engagement through Adaptive Ad Sequencing."
Rafieian, Omid, "Revenue-Optimal Dynamic Auctions for Adaptive Ad Sequencing."
Work in Progress
Rafieian, Omid, "Geographical and Behavioral Information: Complements or Substitutes in Mobile Ad Targeting?"
Rafieian, Omid, "Benefits of Randomization in Online Ad Auctions."
Rafieian, Omid, "Value of User Identifiers in Mobile Ad Targeting."