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Omar Mouchtaki

Assistant Professor of Technology, Operations, and Statistics Stern School of Business, New York University
Email: om2166 (at) stern (dot) nyu (dot) edu

I am an Assistant Professor of Technology, Operations and Statistics at the Leonard N. Stern School of Business at New York University. I received my Ph.D. in Operations Research from the Decision, Risk and Operations Division at Columbia Business School where I was advised by Prof. Omar Besbes and Prof. Will Ma. Prior to Columbia, I earned a BS and MS in Applied Mathematics and Computer Science from Ecole Polytechnique (Paris).

My research develops methodological tools for data-driven decision-making and provides structural insights into operational problems in the data regimes encountered in practice. Using tools from Optimization, Probability, and Statistical Learning, I study core operational problems including inventory, pricing, assortment optimization, and auction design, and aim to provide fine-grained guarantees for algorithms in these settings. I am increasingly interested in the role of artificial intelligence as a foundation for decision-making in complex operational environments.

I teach Operations Management to MBA students at NYU Stern, emphasizing how operational capabilities illuminate firms’ financial performance. I am also co-designing a new course on the foundations of AI agents, examining how agentic and generative AI architectures will transform managerial decisions.

Research

Working/Under-revision Papers

What is the Value of Censored Data? An Exact Analysis for the Data-driven Newsvendor with Rachitesh Kumar

Randomly Wrong Signals: Bayesian Auction Design with ML Predictions with Ilan Lobel and Humberto Moreira
A previous version of this paper was called “Auction Design using Value Prediction with Hallucinations”.

Prior-Independent Bidding Strategies for First-Price Auctions with Rachitesh Kumar

Fast Revenue Maximization with Achraf Bahamou and Omar Besbes

Published or Forthcoming Journal Articles

From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms with Omar Besbes and Will Ma
Management Science (Articles in Advance)

Joint Assortment and Inventory Planning under the Markov chain Choice Model with Omar El Housni, Guillermo Gallego, Vineet Goyal, Salal Humair, Ali Sadighian, Sangjo Kim and Jingchen Wu
Management Science, Vol. 72, No. 10, pp. 8667-8682, 2026

Beyond IID: Data-Driven Decision-Making in Heterogeneous Environments with Omar Besbes and Will Ma
Management Science, Vol. 71, No. 12, pp. 10538-10555, 2025

How Big Should Your Data Really Be? Data-Driven Newsvendor: Learning One Sample at a Time with Omar Besbes
Management Science, Vol. 69, No. 10, pp. 5848-5865, 2023

  • Finalist, ‘Best OM Paper in Management Science’ Award, 2024
  • First Place, RMP Jeff McGill Student Paper Award, 2021
  • Finalist, INFORMS George Nicholson Student Paper Competition, 2021
  • Finalist, APS Best Student Paper Award, 2021

Refereed Conference Proceedings

Prior-Independent Bidding Strategies for First-Price Auctions with Rachitesh Kumar Proceedings of the 2025 ACM Conference on Economics and Computation, 2025

Beyond IID: Data-Driven Decision-Making in Heterogeneous Environments with Omar Besbes and Will Ma Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS), 2022

Teaching

  • Foundations of AI Agents (MBA Elective, course co-designer). More information and student projects are available here.
  • Operations Management (Full-time MBA, Part-time MBA and Tech MBA core)
  • Convex Optimization (PhD Core)