Esmaeil Keyvanshokooh

Assistant Professor
Department of Information & Operations Management
Research Affiliate Faculty, Texas A&M Data Science Institute


Esmaeil Keyvanshokooh is an Assistant Professor of Information & Operations Management at Mays Business School at Texas A&M University.

Dr. Esmaeil Keyvanshokooh studies the research problems that lie at the interface of statistical machine learning, sequential decision-making, and data-driven dynamic optimization. In particular, he is broadly interested in developing personalized data-driven analytical methods for a wide range of healthcare and business analytics applications to yield insights and new functionality. He was the finalist for both the 2021 INFORMS Manufacturing & Service Operations Management (MSOM) Best Student Paper, and the 2021 INFORMS Health Applications (HAS) Best Student Paper awards, and also the finalist for the 2022 POMS Healthcare Operations Management (CHOM) Best Paper. He was the second-place winner for the 2020 INFORMS Decision Analysis Society (DAS) Best Paper Award. He won both the 2020 Katta G. Murty Best Paper Award on Optimization, and the 2019 Richard Wilson Best Paper Award on Service Operations. He has received several other awards, including the 2017 IOE Bonder Fellowship in Applied Operations Research and the prestigious 2020 University of Michigan Rackham Predoctoral Fellowship. Dr. Keyvanshokooh obtained a Ph.D. degree from the University of Michigan, Ann Arbor in 2021. He received his M.Sc. degrees in Statistics from the University of Michigan, and in Operations Research from Iowa State University. He worked as a Machine Learning & Operation Research analyst and engineer at Norfolk Southern Corporation, Atlanta, Georgia.

His research has appeared in journals such as Operations Research, Manufacturing & Service Operations Management (MSOM), and Production and Operations Management (POM).

He has been a referee for Management Science, Operations Research, Manufacturing & Service Operations Management (MSOM), Production and Operations Management (POM), and Journal of Operations Management.

Research Interests

Data-Driven Optimization, Statistical Machine Learning, and Artificial Intelligence with Applications in Healthcare Operations, Precision Medicine, Supply Chain, and Business Analytics