Mohammad Mehdi Fazel Zarandi


Mohammad Mehdi Fazel Zarandi

Support Staff

Get in Touch



Academic Groups

Academic Area

Mohammad Fazel‐Zarandi is a Senior Lecturer at the MIT Sloan School of Management. His research interests are in the areas of data-driven decision-making and machine learning, with applications in public policy. His current research focuses on the use of analytics to analyze and improve decision-making in the public policy domain. Some of the topics he studies include immigration policy, criminal justice, and public health.

Mohammad's research has been published in flagship journals such as Operations Research and Management Science, and has been reported by the Los Angeles Times, the Boston Globethe New York Timesthe Washington Post, Bloomberg, and many other major media outlets.

He has taught various analytics courses to MBA and undergraduate students and has received numerous teaching awards at MIT and Sloan, including the Outstanding Teacher Award at Sloan School of Management (2023) and the MIT Graduate Teaching Award (2023).

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.


Fazel-Zarandi named MIT Sloan School Teacher of the Year

May 8, 2024

Fazel-Zarandi wins Outstanding Teacher Award


"Can Firms Benefit from Competition?"

Fazel-Zarandi, Mohammad, Ignatius Horstmann, and Frank Mathewson. International Journal of Industrial Organization Vol. 76, (2021): 102740.

"Almost Robust Discrete Optimization."

Baron, Opher, Oded Berman, Mohammad M. Fazel-Zarandi, and Vahid Roshanaei. European Journal of Operational Research Vol. 276, No. 2 (2019): 451-465.

"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.

"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 Vol. 13, No. 9 (2018): e0201193.

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

Fazel-Zarandi, Mohammad, and Edward H. Kaplan. Operations Reseach Vol. 66, No. 5 (2018): 1423-1432.

"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.

Load More

Recent Insights

Ideas Made to Matter

8 steps to a stronger cybersecurity strategy

Use this framework to limit operational damage and monetary losses.

Read Article
Ideas Made to Matter

Undocumented immigrant population roughly double current estimate

New research shows the population could have been mismeasured for decades.

Read Article

Media Highlights