Scholar
Esmaeil Keyvanshokooh
Google Scholar ID: U178RcIAAAAJ
Mays Business School, Texas A&M University
Citations & Impact
Citations
1,671
H-index
13
i10-index
13
Publications
20
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- Mays Business School Emerging Scholar Award for Research Excellence, 2025; Second place, POMS College of Healthcare Operations Management (CHOM) Best Paper Competition, 2024; Research Grant Award ($1M) from NIH, 2023-2025; Finalist, POMS CHOM Best Paper Competition, 2022; Finalist, INFORMS MSOM Best Student Paper Competition, 2021; Finalist, INFORMS Health Applications Society (HAS) Best Student Paper Competition, 2021; Second place, INFORMS Decision Analysis Society (DAS) Best Student Paper Competition, 2020; Winner, IOE Katta G. Murty Prize for Best Student Paper on Optimization, 2020; Winner, IOE Richard C. Wilson Prize for Best Student Paper on Service Systems, 2019; Winner, IOE Bonder Fellowship Award in Applied Operations Research, 2017; University of Michigan Rackham Pre-doctoral Fellowship Award, 2019.
Research Experience
- Worked as a Machine Learning & Operation Research Analyst at Norfolk Southern Corporation, Atlanta, Georgia before Ph.D. studies.
Education
- Ph.D. in Operations Research from the University of Michigan, Ann Arbor, May 2021; M.A. in Statistics from the University of Michigan; M.S. in Industrial Engineering and Operations Research from Iowa State University.
Background
- Assistant Professor of Information & Operations Management at Mays Business School, Texas A&M University. Also a research affiliate faculty at Texas A&M Data Science Institute (TAMIDS) and Texas A&M Telehealth Institute. Research focuses on developing data-driven decision-making methodologies that integrate machine learning theory, artificial intelligence tools, and optimization algorithms, with particular emphasis on establishing rigorous theoretical performance guarantees. Applications include healthcare and public policy, service operations, digital health, precision medicine, and societal impacts of AI and ML.
Miscellany
- Academic Collaborators: Actively taking on more projects. If you have an interesting problem that intersects with some of my work and interest, or are looking for a new problem, please reach out over email. Prospective Students: Always looking for PhD and master students with strong backgrounds in machine learning theory and data-driven optimization. Please reach out over email if you are interested.
Co-authors