My research focuses on data-driven decision making and dynamic optimization, particularly used to design and analyze marketplaces and online platforms. I combine tools from online and stochastic optimization, machine learning, and game theory to improve the operations of modern marketplaces such as online retail and the gig economy. I am also interested in using these techniques to optimize broader matching systems such as organ allocation, blood banks, and emergency response.
Papers
"Adaptive Approximation Schemes for Matching Queues" with Ali Aouad and Amin Saberi, submitted
[arXiv][video]
Appeared in the 57th Annual ACM Symposium on Theory of Computing (STOC'25).
Accepted for presentation at 2025 Marketplace Innovation Workshop.
"Spatial Matching under Multihoming" with Ali Aouad and Daniel Freund, Operations Research (major revision)
[SSRN][poster]
Accepted for presentation at 2023 Marketplace Innovation Workshop.
"Improved Approximations for Stationary Bipartite Matching: Beyond Probabilistic Independence" with Ali Aouad, Tristan Pollner, and Amin Saberi, submitted
[arXiv]
"Governance of Social Welfare in Networked Markets" with Mohammadamin Fazli, IEEE Transactions on Computational Social Systems
[published]
"On the maximum order of induced paths and induced forests in regular graphs" with Saeeid Akbari, Sepehr Mousavi, Hessam Nikpey, Soheil Sheybani
[arXiv]
Work in progress
"Stationary Bipartite Matching with Stochastic Rewards" with Ali Aouad and Amin Saberi, in preparation.
"Learning a Choice Model for Bundles via Neural Networks" with Ali Aouad, Vincent Auriau, and Antoine Desir.
"Bayesian Learning in Online Stochastic Matching" with Thomas Kesselheim and Amin Saberi.
"Curbing Cherry-picking through Priority Dispatch" with Jiayu (Kamessi) Zhao.