Mohsen Bayati

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

Additional Administrative Titles

Spence Faculty Scholar for 2015-2016

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.

Bio

Mohsen Bayati 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 support. 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 co-awarded the INFORMS Healthcare Applications Society best paper (Pierskalla) award in 2014, INFORMS Applied Probability Society best paper award in 2015, and National Science Foundation CAREER award in 2016.

Awards and Honors

  • Spence Faculty Scholar for 2015-16

Publications

Journal Articles

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. Forthcoming in Operations Research. 2015.
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.

Teaching

Degree Courses

2016-17

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...

2015-16

GSB students are eligible to report on work experience that is relevant to their core studies under the direction of the Director of the PhD Program. Registration for this work must be approved by the Director of the PhD Program and is limited to...

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

March 7, 2016
Lax reporting requirements make it easier to change records.
May 5, 2014
A scholar shows how data analysis can help lower patients’ risk of hospital readmission.