Mohammad Mehdi Fazel Zarandi


Mohammad Mehdi Fazel Zarandi


Mohammad Fazel‐Zarandi is a Senior Lecturer at the MIT Sloan School of Management.  

His research interests are in the areas of public sector operations, data-driven decision making, statistical learning, and applied probabilistic modeling and statistics. 

His current research focuses on the use of operations techniques to improve decision-making in the public policy domain. Some of the topics he studies include immigration policy and fairness in public systems. 

Mohammad's research has been reported by the Los Angeles Times, The Boston Globe, The New York Times, Bloomberg, and many other major media outlets.

Before joining MIT, Mohammad was a Postdoctoral Associate and Lecturer at the Yale School of Management. He received his PhD from the Rotman School of Management, University of Toronto in operations management and his MS in industrial engineering, also from the University of Toronto.


"Approximating the First-Come, First-Served Stochastic Matching Model with Ohm's Law."

Fazel-Zarandi, Mohammad, and Edward H. Kaplan. Operations Reseach. Forthcoming.

"The Number of Undocumented Immigrants in the United States: Estimates Based on Demographic Modeling with Data from 1990-2016."

Fazel-Zarandi, Mohammad, Edward H. Kaplan, and Jonathan S. Feinstein. PLOS ONE. Forthcoming.

"Truthful Cheap Talk: Why Operational Flexibility May Lead to Truthful Communication."

Berman, Oded, Mohammad Fazel-Zarandi, and Dmitry Krass. Management Science Vol. 65, No. 4 (2019): 1624-1641.

"Solving a Stochastic Facility Location/Fleet Management Problem with Logic-Based Benders' Decomposition."

Fazel-Zarandi, Mohammad, Oded Berman, and J. Christopher Beck. IIE Transactions Vol. 45, No. 8 (2012): 896-911.

"Using Logic-Based Benders Decomposition to Solve the Capacity- and Distance-Constrained Plant Location Problem."

Fazel-Zarandi, Mohammad, and J. Christopher Beck. INFORMS Journal on Computing Vol. 24, No. 3 (2012): 387-398.

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