Scholar
Omar Mouchtaki
Google Scholar ID: LSr5-w4AAAAJ
New York University
Citations & Impact
Citations
143
H-index
4
i10-index
4
Publications
8
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- Published or forthcoming journal articles include 'Beyond IID: Data-Driven Decision-Making in Heterogeneous Environments' (with Omar Besbes and Will Ma), forthcoming in Management Science; 'How Big Should Your Data Really Be? Data-Driven Newsvendor: Learning One Sample at a Time' (with Omar Besbes), published in Management Science, Vol. 69, No. 10, pp. 5848-5865, 2023. This paper was a finalist for several awards including the 'Best OM Paper in Management Science' Award, 2024, and won the RMP Jeff McGill Student Paper Award, 2021.
Research Experience
- Researcher at the Decision, Risk and Operations Division at Columbia Business School, currently an Assistant Professor at the Stern School of Business, New York University.
Education
- Ph.D. in Operations Research from the Decision, Risk and Operations Division at Columbia Business School, advised by Prof. Omar Besbes and Prof. Will Ma; BS and MS in Applied Mathematics and Computer Science from Ecole Polytechnique (Paris).
Background
- Assistant Professor of Technology, Operations and Statistics at the Leonard N. Stern School of Business at New York University. The goal of his research is to bridge the gap between the theory and the practice of data-driven decision-making by developing methodological tools tailored to the problem at hand and by deriving fine-grained and problem-specific guarantees for algorithms. His current research draws on tools from Optimization, Probability and Statistical Learning and is applied to central operational decision-making processes such as those for inventory, pricing and assortment optimization.
Miscellany
- Teaching: Operations Management (Part-Time MBA Core, Spring 2025).
Co-authors