Mohsen Bayati

Mohsen   Bayati
Associate Professor, Operations, Information & Technology
Contact Info
Associate Professor of Operations, Information & Technology
Associate Professor of Electrical Engineering (by courtesy), School of Engineering

Research Statement

Mohsen Bayati studies probabilistic and statistical models for decision making with large-scale and complex data, and applies them to healthcare problems. He also studies graphical models and message-passing algorithms. For more details see personal website.


Mohsen received a BS in Mathematics from Sharif University of Technology and a PhD in Electrical Engineering from Stanford University in 2007. His dissertation was on algorithms and models for large-scale networks. During the summers of 2005 and 2006 he interned at IBM Research and Microsoft Research respectively. He was a Postdoctoral Researcher with Microsoft Research from 2007 to 2009 working mainly on applications of machine learning and optimization methods in healthcare and online advertising. In particular, he helped develop a system for predicting hospital patient readmissions and obtained a decision support mechanism for allocating scarce hospital resources to post-discharge care. Their system is currently used in several hospitals across US and Europe. He was a Postdoctoral Scholar at Stanford University from 2009 to 2011 with a research focus in high-dimensional statistical learning. In 2011 he joined Stanford University as a faculty, and since 2015 he is an associate professor of Operations, Information, and Technology at Stanford University Graduate School of Business. He was awarded the INFORMS Healthcare Applications Society best paper (Pierskalla) award in 2014 and in 2016, INFORMS Applied Probability Society best paper award in 2015, and National Science Foundation CAREER award.

Awards and Honors

  • Spence Faculty Scholar, 2015-2016


Journal Articles

Hamsa Bastani, Joel Goh, Mohsen Bayati. Management Science. 2017.
Joel Goh, Mohsen Bayati, Stefanos Zenios, Sundeep Singh, David Moore. Operations Research. 2017.
Mohsen Bayati, Andrea Montanari, Amin Saberi. Operations Research. 2017.
Mohsen Bayati, Sonia Bhaskar, Andrea Montanari. Statistical Methods for Medical Research. 2016.
Erjie Ang, Sara Kwasnick, Mohsen Bayati, Erica Plambeck, Michael Aratow. Manufacturing and Service Operations Management (MSOM). November 6, 2015, Vol. 18, Issue 1, Pages 141-156.
Mohsen Bayati, Marc Lelarge, Andrea Montanari. Annals of Applied Probability. April 2015, Vol. 25, Issue 2, Pages 753-822.
Joel Goh, Margaret Bjarnadottir, Mohsen Bayati, Stefanos Zenios. Operations Research. 2015, Vol. 63, Issue 6, Pages 1528-1546.
Mohsen Bayati, Christian Borgs, Jennifer Chayes, Yash Kanoria, Andrea Montanari. Journal of Economic Theory (JET). 2015, Vol. 156, Pages 417-454.
Mohsen Bayati, Mark Braverman, Michael Gillam, Karen M. Mack, George Ruiz, Mark S. Smith, Eric Horvitz. PLoS One. October 2014, Vol. 9, Issue 10.
Mohsen Bayati, David F. Gleich, Amin Saberi, Yin Wang. ACM Transactions on Knowledge Discovery from Data . March 2013, Vol. 7, Issue 1, Pages 2013.
Mohsen Bayati, David Gamarnik, Prasad Tetali. Annals of Probability. 2013, Vol. 41, Issue 6, Pages 4080-4115.
Mohsen Bayati, Andrea Montanari. IEE Transactions on Information Theory. 2012, Vol. 587, Issue 4, Pages 1997-2017.
Mohsen Bayati, Christian Borgs, Jennifer Chayes, Riccardo Zecchina. SIAM J. Discrete Math. 2011, Vol. 25, Issue 2, Pages 989-2011.
Mohsen Bayati, Andrea Montanari. IEEE Transcations on Information Theory. 2011, Vol. 57, Issue 2, Pages 764-785.
Mohsen Bayati, Jeong Han Kim, Amin Saberi. Algorithmica. December 2010, Vol. 58, Issue 4, Pages 860-910.
Mohsen Bayati, Alfredo Braunstein, Riccardo Zecchina. Journal of Mathematical Physics. 2009, Vol. 49.
Mohsen Bayati, Christian Borgs, Jennifer Chayes, Riccardo Zecchina. Journal of Statistical Mechanics: Theory and Experiment. July 20, 2008.
Mohsen Bayati, Devavrat Shah, Mayank Sharma. IEEE Transactions on Information Theory. March 2008, Vol. 54, Issue 3, Pages 1241-1251.
Mohsen Bayati, C. Borgs, A. Braunstein, J. Chayes, A. Ramezanpour, R. Zecchina. Physical Review Letters. 2008.

Working Papers

Low-Acuity Patients Delay High-Acuity Patients in an Emergency Department | PDF
Mohsen Bayati, Sara Kwasnick, Danqi Luo, Erica Plambeck2017
Matrix Completion Methods for Causal Panel Data Models
Susan Athey, Mohsen Bayati, Nick Doudchenko, Guido W. Imbens, Khashayar Khosravi2017
Reducing the Exploration-Exploitation Tradeooff in Contextual Bandits | PDF
Hamsa Bastani, Mohsen Bayati, Khashayar Khosravi2017


Degree Courses


The objective of this course is to analyze real-world situations where significant competitive advantage can be obtained through large-scale data analysis, with special attention to what can be done with the data and where the potential pitfalls...

This aim of this course is to cover modern tools for data-driven decision making. Most decision making tasks involve uncertainty that is directly impacted by the amount and complexity of data at hand. Classical decision models rely on strong...


The objective of this course is to analyze real-world situations where significant competitive advantage can be obtained through large-scale data analysis, with special attention to what can be done with the data and where the potential pitfalls...

Data for Action is an MBA compressed course dedicated to identifying value in and creating value from data. It deals with the technical, legal, regulatory and business strategic decisions that must be considered when delivering solutions to...

Stanford University Affiliations

Greater Stanford University

Insights by Stanford Business

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May 5, 2014
A scholar shows how data analysis can help lower patients’ risk of hospital readmission.