Mohsen Bayati has two main research interests: large-scale statistical data-mining, and applications of information technology in healthcare. In particular, he uses tools from graph theory, machine learning, probability, and statistical physics in data-driven healthcare (predictive models, optimization, and decisions), high dimensional statistics, and networks.

Mohsen Bayati received his PhD in Electrical Engineering from Stanford University in 2007. His dissertation was on machine learning and modeling aspects of 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.

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