Algorithms and AI Can Make Hiring More Diverse
The cost is likely minimal to achieve a fairer outcome.
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Biography
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
"Batching and Optimal Multi-stage Bipartite Allocations", with Yiding Feng, Management Science, 2024 SSRN preprint: 3689448 (preliminary conference version in ITCS'21)
"Near-optimal Bayesian Online Assortment of Reusable Resources", with Yiding Feng and Amin Saberi, Operations Research, 2024 (preliminary conference version in ACM EC’22)
"Dynamic Matching with Post-allocation Service and its Application to Refugee Resettlement", with Kirk Bansak, Soonbong Lee, Vahideh Manshadi, and Elisabeth Paulson, major revision from Management Science, SSRN preprint:4748762 (preliminary conference version in ACM EC'24)
"Online Matching with Cancellation Costs", with Farbod Ekbatani and Yiding Feng, major revision from Operations Research, SSRN preprint: 4245468 (preliminary conference version in ACM EC’23)
"Markovian Search with Socially Aware Constraints", with Mohammad Reza Aminian and Vahideh Manshadi, major revision from Management Science, SSRN preprint: 4347447
"Robustness of Online Inventory Balancing Algorithm to Inventory Shocks", with Yiding Feng and Amin Saberi, major revision from Management Science, SSRN preprint: 3795056
"Prophet Inequalities with Cancellation Costs", with Farbod Ekbatani, Pranav Nuti, and Jan Vondrak, SSRN preprint: 4779633 (preliminary conference version in ACM STOC'24)
"Online Job Assignment", with Farbod Ekbatani, Yiding Feng, and Ian Kash, SSRN preprint: 4745629 (preliminary conference version in MSOM Supply Chain SIG 2024)
"Misalignment, Learning, and Ranking: Harnessing Users Limited Attention", with Arpit Agarwal and Prathamesh Patil, SSRN preprint: 4365381
"Robust Dynamic Staffing with Predictions", with Yiding Feng and Vahideh Manshadi, SSRN preprint: 4732158 (preliminary conference version in MSOM Service SIG 2024)
"Linear Programming Based Near-Optimal Pricing for Laminar Bayesian Online Selection", with Nima Anari, Ali Shameli, and Amin Saberi, minor revision from Mathmetics of Operations Research, SSRN preprint: 3430156 (preliminary conference version in ACM EC'19)
"Bernoulli Factories for Flow-Based Polytopes", with With Jon Schneider and Renato Paes Leme, SIAM Journal on Discrete Mathematics (SIDMA), 2023.
"Correlated Cluster-Based Randomized Experiments: Robust Variance Minimization", with Chen Chen and Ozan Candogan, Management Science, 2023 (preliminary conference version in ACM EC’23)
"Online Bipartite Matching with Reusable Resources", with Steven Delong, Alireza Farhadi, Balu Sivan, and Rajan Udwani, Mathematics of Operations Research, 2023 (preliminary conference version in ACM EC’22)
"Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes", with Yuval Emek, Ron Lavi and Yangguang Shi, Mathematics of Operations Research (preliminary conference version in NeurIPS'20)
"Two-stage Stochastic Matching and Pricing with Applications to Ride Hailing", with Yiding Feng and Amin Saberi, Operations Research, 2023 (preliminary conference version in ACM-SIAM SODA’21, spotlight talk in RMP’21 conference)
"Fair Dynamic Rationing", with Vahideh Manshadi and Scott Rodilitz,
Management Science, 2023 (preliminary conference version in ACM EC’21, spotlight talk in RMP’21 conference)
"Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimiza-tion", with Negin Golrezaei, Joshua Wang, Fransisca Susan and Ashwinkumar Badanidiyuru, Management Science, 2022 (preliminary conference version in ACM EC’21)
"Sequential Submodular Maximization and Applications to Ranking an Assortment of Products", with Arash Asadpour, Amin Saberi and Ali Shameli,
Operations Research, 2022 (preliminary conference version in ACM EC’22)
"Combinatorial Bernoulli Factories", with Renato Paes Leme and Jon Schneider, Bernoulli (Journal of the Bernoulli Society), 2023 (preliminary conference version in ACM STOC’21)
"Bernoulli Factories and Black-Box Reductions in Mechanism Design", with Shaddin Dughmi, Jason Hartline and Robert Kleinberg, Journal of the ACM, 2021 (preliminary conference version in ACM STOC’17, presented at 6th World Congress of the Game Theory Society — GAMES’21)
"Fast Core Pricing for Rich Advertising Auctions", with Jason Hartline, Mohammad Reza Khani, Nicole Immorlica, and Brendan Lucier, Operations Research (OR), 2020 (preliminary conference version in ACM EC’19)
"Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization", with Tim Roughgarden and Joshua Wang,
Journal of Machine Learning Research, 2020 (preliminary conference version in NeurIPS’18, oral presentation)
"Multi-scale Online Learning and its Applications to Online Auctions", with Se´bastien Bubeck, Nikhil Devanur and Zhiyi Huang, Journal of Machine Learning Research, 2019 (preliminary conference version in ACM EC’17)
"Descending Price Auctions with Bounded Number of Price Levels and Batched Prophet Inequality", with Saeed Alaei, Ali Makhdoumi, and Azarakhsh Malekian, in Proc. 23rd ACM conference on Economics and Computation (ACM EC 2022)
Working Papers
2025 - 2026 Course Schedule
Get Insights from Rad Niazadeh in Chicago Booth Review
A Hiring Algorithm That Isn’t Vulnerable to Biased Data
Research explores a way to make AI recruiting decisions more fair.
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A Tool to Help Algorithms Cope with Human ‘Gaming’
User attempts to outwit algorithms can lead to less-optimal outcomes for everyone.
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