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 Operations Research, Journal of Machine Learning Research, Games and Economic Behavior, Journal of the ACM, and in (peer-reviewed) top conference proceedings such as ACM STOC, IEEE FOCS, NeurIPS, ICML, ACM EC, ACM-SIAM SODA and ITCS.
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.
Batching and Optimal Multi-stage Bipartite Allocations”, with Yiding Feng, Available at 3689448 (preliminary conference version in ITCS 2021)
"Two-stage Stochastic Matching and Pricing with Applications to Ride Hailing”, with Yiding Feng and Amin Saberi, Available at SSRN 3613755 (preliminary conference version in SODA 2021)
"Bernoulli Factories and Black-Box Reductions in Mechanism Design”, with Shaddin Dughmi, Jason Hartline and Bobby Kleinberg, Journal of the ACM (JACM), 2020.
"Fast Core Pricing for Rich Advertising Auctions", with Jason Hartline, Mohammad Reza Khani, Nicole Immorlica, and Brendan Lucier, Operations Research (OR), 2020.
"Multi-scale Online Learning: Theory and Applications to Online Auctions and Pricing", with Sébastien Bubeck, Nikhil Devanur and Zhiyi Huang, Journal of Machine Learning Research (JMLR), 2019.