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Rad Niazadeh is an Assistant Professor of Operations Management and Asness Junior Faculty Fellow at Chicago Booth. He is also part of the faculty at the Toyota Technological Institute at Chicago (TTIC) by a courtesy appointment. Prior to joining Chicago Booth, he was a visiting researcher at the Google Research NYC's market algorithms team, and a postdoctoral fellow at Stanford University, Computer Science. He received his PhD in Computer Science, with a minor in Applied Mathematics, from Cornell University.

Rad studies the interplay between algorithms (for computation), data (for learning), and incentives (for modeling strategic behavior) in real-time operations management. His primary research goal is to build theoretical methodologies and application-based frameworks for data-driven sequential decision-making in complex and dynamic operational scenarios, mostly related to the operations of online platforms, electronic markets, and modern non-profit organizations. On the practical side, he utilizes the theory to develop (i) computationally and economically efficient real-time market algorithms and (ii) socially-aware decision-making policies that prioritize equity, fairness, and non-discrimination in the operations of non-profit organizations, governmental agencies, and online platforms.

Professor Niazadeh’s research has been published in journals such as Management Science, Operations Research, Mathematics of Operations Research, Journal of Machine Learning Research, Games and Economic Behavior, Journal of the ACM, Bernoulli, and in (peer-reviewed) top conference proceedings in computer science such as ACM STOC, IEEE FOCS, NeurIPS, ICML, ACM EC, ACM-SIAM SODA and ITCS.

Rad has received the INFORMS Auctions and Market Design Michael H. Rothkopf Junior Researcher Paper Prize (first place) in 2021, INFORMS Data Mining and Decision Analytics Best Paper Award (third place) in 2021, 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.

Research Interests

Online algorithms and optimization in markets and platforms; Algorithmic mechanism design and game theory; Online learning theory and applications in operations management; Algorithmic aspects of machine learning and data science in management

Academic Areas

  • Operations Management

Selected Publications

Working Papers

2023 - 2024 Course Schedule

Number Course Title Quarter
36922 Online Learning, Operations, and Electronic Markets 2024 (Spring)
36600 Workshop in Operations/Management Science 2023 (Autumn)
36600 Workshop in Operations/Management Science 2024 (Spring)

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