Rad Niazadeh studies the interplay between algorithms, incentives, and learning, with a keen focus on theoretical aspects of operations research and market design. His primary research goal is to use such tools and techniques as algorithm design, mechanism design, game theory, online optimization, and online learning in order to improve the economics and operations of online marketplaces and e-commerce platforms. Economical goals of interest include revenue, social welfare, and fairness; and operational goals of interest include efficiency in allocations, revenue management, and improved decision making.
Prior to joining Chicago Booth, Niazadeh was a visiting faculty researcher in the market algorithms group at Google Research in New York City. Previously, he held a Motwani Postdoctoral Fellowship in theoretical computer science at Stanford University. He received his PhD in computer science, with a minor in applied mathematics, from Cornell University, where he was awarded an Irwin M. and Joan K. Jacobs Fellowship. He earned an honorable mention for the INFORMS Revenue Management and Pricing Section Dissertation Prize and was a recipient of the Google PhD Fellowship in Market Algorithms. He was a research intern at Microsoft Research New England, Microsoft Research Redmond, and Yahoo! Research lab during his PhD studies. He completed his BSc and MSc degrees in electrical engineering at Sharif University of Technology in Tehran, Iran.
Photo credit: Reza Shahbazi