Rad Niazadeh is an assistant professor in Operations Management. He studies the interplay between algorithms, incentives, and learning in online marketplaces, with a focus on applications in revenue management and market design. In particular, he is interested in algorithmic mechanism design and game theory, online learning theory and applications, online algorithms, and algorithmic aspects of machine learning in operations research.
Prior to joining Chicago Booth, Rad was a Motwani postdoctoral fellow at Stanford University (department of Computer Science) and a visiting researcher at Google Research NYC (market algorithms team). He received his PhD in Computer Science from Cornell University (minored in Applied Mathematics). Additionally, he holds M.Sc. and B.Sc. degrees in Electrical Engineering from Sharif University of Technology.
Professor Niazadeh’s research has been published in journals such as Journal of Machine Learning Research, Games and Economic Behavior, Journal of the ACM (to appear), Operations Research (to appear), and in peer-reviewed conference proceedings such as ACM STOC, IEEE FOCS, NeurIPS, ICML, ACM EC, and SIAM SODA.
Rad has received the INFORMS Revenue Management and Pricing Dissertation Award (honorable mention) in 2018, the Google PhD Fellowship in Market Algorithms in 2016, Stanford Motwani fellowship in 2017, and Cornell Jacobs fellowship in 2012.