My research focuses on dynamic and data-driven 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
"Near-optimal Adaptive Policies for Dynamic Matching Markets" with Ali Aouad and Amin Saberi, Operations Research (major revision)
[arXiv][video]
Appeared in the 57th Annual ACM Symposium on Theory of Computing (STOC'25).
"Spatial Matching under Multihoming" with Ali Aouad and Daniel Freund, Operations Research (major revision)
[SSRN][poster]
"Improved Approximations for Stationary Bipartite Matching: Beyond Probabilistic Independence" with Ali Aouad, Tristan Pollner, and Amin Saberi, submitted
[arXiv]
Accepted in the 27th ACM Conference on Economics and Computation (EC'26).
"Stationary Bipartite Matching with Stochastic Rewards" with Ali Aouad and Amin Saberi, working paper.
"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
"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.