You are here

Mohsen Bayati

Mohsen   Bayati
Assistant Professor, Operations, Information & Technology
MohsenBayati
Assistant Professor of Operations, Information and Technology
Assistant Professor of Electrical Engineering (by courtesy), School of Engineering

Research Statement

Mohsen Bayati has two main research interests: machine learning and statistical models for large data, and their applications in healthcare. In particular, he designs methods based on graphical models, probability theory, and statistical physics, and applies them in data-driven healthcare (predictive models, optimization, and decisions). For more details see personal website.

Bio

Mohsen Bayati received his 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 focused on hospital readmissions. Nearly one in every five patients is readmitted to the hospital within 30 days of their discharge. The estimated cost of unplanned rehospitalizations to Medicare in 2004 was around $17.4 billion. Mohsen Bayati and his colleagues at Microsoft Research applied machine learning methods to hundreds of thousands of hospital electronic health records to identify patients with the highest risk of being rehospitalized and obtained a decision support mechanism for allocating scarce resources to post-discharge support. Their system is currently used in several hospitals across US and Europe.

He has been a Postdoctoral Scholar at Stanford University from 2009 to 2011 with a research focus in high-dimensional statistical learning.

Publications

Journal Articles

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, Christian Borgs, Jennifer Chayes, Yash Kanoria, Andrea Montanari. Journal of Economic Theory (JET). March 2014, Vol. 156, Pages 417-454.
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, Marc Lelarge, Andrea Montanari. Annals of Applied Probability. March 2013, Vol. 25, Pages 753-822.
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

Estimating LASSO Risk and Noise Level - Preliminary Version | PDF
Mohsen Bayati, Murat T. Erdogdu, Andrea Montanari2015
Active Postmarketing Drug Surveillance for Multiple Adverse Events | PDF
Joel Goh, Margret V. Bjarnadottir, Mohsen Bayati, Stefanos A. Zenios2013

Courses Taught

Degree Courses

2014-15

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...

This course aims to introduce students to research topics in data-driven decision making with specific attention to healthcare applications. However, most concepts are applicable in areas beyond healthcare as well. Examples of topics are:...

2013-14

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 course aims to introduce students to research topics in evidence-based healthcare. The course covers topics: clinical decision support, predictions and risk adjustments, high dimensional statistical models, computational methods for large-...

Insights by Stanford Business

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