Dr. Mohsen Bayati

Associate Professor

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Biography

Mohsen Bayati is the Carl Marilynn Thoma Professor and R. Michael Mary Shanahan Faculty Fellow at Stanford University's Graduate School of Business. He holds courtesy appointments in the Departments of Electrical Engineering and Radiation Oncology. His research focuses on the application of artificial intelligence in healthcare, particularly through data-driven learning and decision models aimed at enhancing healthcare outcomes. Bayati is focused on ensuring that AI systems align with societal objectives and develop robust graphical models that utilize message-passing algorithms. In addition, he investigates mathematical modeling in decision-making processes, striving to create algorithms that provide personalized learning and decision-making support. He serves as an associate editor for the journal Management Science, engaging in the intersections of data science, healthcare management, and stochastic systems. Bayati has taught multiple courses at Stanford, including Business Intelligence and Data Learning Decision-Making, reflecting his comprehensive expertise in applying data science to real-world challenges.

Research Interests

Experience

Carl Marilynn Thoma Professor

— Present

Stanford University Graduate School of Business • Stanford, CA

Professor specializing in AI applications and data-driven decision-making in healthcare.

Courses

OIT 367 Business Intelligence OIT 604 Data, Learning, Decision-Making OIT 536 Data Action: Insights Applications OIT 267 Data Decisions - Accelerated

Requirements for Stanford University

Doctorate Program
Requirements
GPA Requirement
Required:3.5
TOEFL
Listening
Required:26
Reading
Required:26
Writing
Required:26
Speaking
Required:26
Total
Required:100
GRE General
Verbal
Required:160
Quantitative
Required:165
Analytical Writing
Required:4.5
Overall
Required:4.5
Prerequisites
Bachelor degree from an accredited institution Strong background in mathematics and programming
Application Checklist
  • Statement of Purpose
  • Three letters of recommendation
  • Official transcripts
  • Resume/CV
Specialization Notes

The Computer Science department emphasizes research potential. GRE General is currently optional but recommended for some tracks.