Mohsen Bayati is The Carl and Marilynn Thoma Professor of Operations, Information & Technology at Stanford Graduate School of Business. He holds courtesy appointments in Electrical Engineering and Radiation Oncology at Stanford University. His research focuses on applying machine learning, statistical physics, and decision models to healthcare and experimental design. Key contributions include reducing hospital readmissions via predictive analytics, developing human-in-the-loop AI systems like MEDIC for medication directions, and advancing causal inference methods for experiments with network interference. Education: PhD (Stanford University, Electrical Engineering), MSc (Stanford University, Operations Research), BSc (Sharif University of Technology, Mathematics). Research Interests: Machine learning in healthcare, causal inference, data-driven decision-making, and algorithmic efficiency. Current projects include optimizing experimental designs for staggered rollouts and improving prostate cancer recurrence predictions through clinical expertise integration with ML. Awards: 2024 PhD Faculty Distinguished Service Award, Younger Family Scholarships (2019–21), Spence Scholar (2015–16). Notable collaborations include work with Amazon, Microsoft Research, and Stanford Medical School. Advising: Mentored PhD students like Sadegh Shirani (causal message-passing) and Ruoxuan Xiong (optimal experimental design). Taught courses on business intelligence, data analysis, and operations research. Labs/Teams: Active in Stanford’s Golub Capital Social Impact Lab and collaborates with the FDA on drug interaction analysis using EHR data.
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