I am an Assistant Professor at the Department of Computational and Data Sciences at George Mason University.  Prior to joining George Mason University, I was an Assistant Professor at the Department of Industrial Engineering at the University of Pittsburgh. Before, I was a lecturer in the Operations Research and Statistics Group at the MIT Sloan School of Management and a postdoctoral research associate in Operations Research at MIT. I hold a PhD degree in Applied Mathematics from MIT.

My overarching career goal is to leverage operations research, data analytics, and artificial intelligence to serve society. Specifically, my research program aims to achieve this goal by 1) advancing the underlying mathematical and computational techniques, such as data-driven, robust, and distributionally robust optimization, and their interface with machine learning to enable the design of data-driven models suitable for a wide range of applications.; and 2) applying these methods to real-world complex decision-making problems. I have worked on various applications, including kidney exchange,  epidemic modeling and analysis, rare-event modeling and prediction, supply chain operations, WHO-EPI vaccine distribution, and online resource allocation. I have assembled a diverse research team of six PhD students working toward these research directions. My research program has received support from the Office of Naval Research, NSF, and DARPA.

Recent News

Feb. 2025: Our Ph.D. student, Yingze Hou, who is co-advised with Taposh Banerjee, will defend his Ph.D. dissertation in February, 2025.

Jan. 2025: Our paper "Equity-driven facility location problem under uncertainty: a robust optimization approach" published in the Computers & Operations Research Journal.

Dec. 2024: A new paper Robust  quickest change detection with sampling control.

Dec. 2024: We have received the 2024 Best Paper Award from the Editors of OMEGA journal and the Directors of Elsevier, in recognition of your significant contribution to the Journal for our paper   Data-driven inventory policy: Learning from sequentially observed non-stationary data.

Nov. 2024:  A new paper  Robust quickest change detection in multi-stream non-stationary processes 

Current Funding:

Collaborative Research: ATD: Robust Quickest Threat Detection in Non-Stationary Multi-Stream Data. U.S. National Science Foundation (NSF) (PI),  Sept 2024 - Aug 2027.

Time-Aware and Distributed Continual Learning. Office of Naval Research (ONR) (Sole PI), Aug 2023 - Aug 2026.

Selected publications

• Robust quickest change detection in non-stationary processes. Y. Hou, Y. Oleyaeimotlagh, R. Mishra, H. Bidkhori, and T. Banerjee.  Sequential Analysis, 2024.   

• Equity-driven facility location problem under uncertainty: a robust optimization approach. M. Li*, A. A. Digehsara, A. Ardestani-Jaafari, and H. Bidkhori. Computers & Operations Research (Accepted), 2025.

• Multi-feature SEIR model for epidemic analysis and vaccine prioritization. Y. Hou*, and H. Bidkhori.  PLOS ONE, 2024.

• Distributionally robust optimization as a scalable framework to characterize extreme value distributions. P. Kuiper*, V. Tarokh, W. Yang, J. Blanchet, A. Hasan*, Y. Ng*, and H. Bidkhori. The Conference on Uncertainty in Artificial Intelligence, 2024.

• Data-driven inventory policy: Learning from sequentially observed non-stationary data. K. Ren*, H. Bidkhori, and Z.J.M. Shen.   OMEGA - The International Journal of Management Science, 2024.           2024 OMEGA Best Paper Award 

• A study on distributionally robust optimization with incomplete joint data. K. Ren*, and H. Bidkhori. European Journal of Operational Research, 2023 .

• Feature-modified SEIR model for pandemic simulation and evaluation for intervention approaches. Y. Hou*, and H. Bidkhori.  Proceedings of the Winter Simulation Conference (WSC), 2022. (Nominated for the best student paper award)

• Improving robustness: when and how to minimize or maximize the loss variance. V. Balaban*, H. Bidkhori, and P. Pogdan.   21st IEEE International Conference on Machine Learning and Applications (ICMLA), 2022. 

• Distributionally robust cycle and chain packing with application to organ exchange. D. C.  McElfresh*, K. Ren*, H. Bidkhori, and J. P. Dickerson.  Proceedings of the Winter Simulation Conference (WSC), 2021.

• Data-driven two-stage stochastic programming with marginal data. K. Ren*, and H. Bidkhori. Proceedings of the Winter Simulation Conference (WSC), 2021.

• Optimizing vaccine distribution in low and middle-income countries. Y. Yang*, H. Bidkhori, and J. Rajgopal. OMEGA - The International Journal of Management Science, 2021.

• Kidney exchange with inhomogeneous edge existence uncertainty. H. Bidkhori, J. P. Dickerson, D. C. McElfresh*, and K. Ren*. The Conference on Uncertainty in Artificial Intelligence, 2020.

• Analysis of process flexibility designs under disruptions. E. Mehmanchi*, H. Bidkhori, and O. Prokopyev. IISE Transactions, 2020.   

• Scalable robust kidney exchange. D.C. McElfresh*, H. Bidkhori, and J.P. Dickerson. Conference on Artificial Intelligence (AAAI), 2019.

• Analyzing process flexibility: a distribution-free approach with partial expectation. H. Bidkhori, D. Simchi-Levi, and Y. Wei. Operations Research Letters, 44 (3), 291-296, 2016.

• On the performance of affine policies for two-stage adaptive optimization: a geometric perspective.  D. Bertsimas, and H. Bidkhori.  Mathematical Programming,  2015.