Esmaeil Keyvanshokooh

Esmaeil Keyvanshokooh 

Esmaeil Keyvanshokooh
Ph.D. Candidate in Operations Research
Department of Industrial & Operations Engineering
University of Michigan at Ann Arbor
E-mail: keyvan-at-umich-dot-edu
Google Scholar
Twitter
Linkedin
ResearchGate

About Me

I am a final year Ph.D. candidate in Operations Research at the Department of Industrial & Operations Engineering of the University of Michigan at Ann Arbor. I have the pleasure of being advised by Professor Mark P. Van Oyen and Professor Cong Shi. I earned a MSc in Statistics at the Department of Statistics of the University of Michigan. Prior to joining UMICH, I earned a MSc in Industrial Engineering and Operations Research from the Iowa State University, and worked as a Machine Learning & Operation Research Intern at Norfolk Southern Corporation, Atlanta, Georgia.

About My Research Interests

  • My research interests lie at the interface of statistical machine learning, artificial intelligence, and data-driven optimization. In particular, I develop personalized data-driven sequential decision-making methodologies under an uncertain environment and prove theoretical performance guarantees for them. These methodologies learn from sequentially collected observational data over time to make efficient and effective online (real-time) decisions for each individual as a function of the individual features.

    • Methodologies: Data-Driven Optimization, Statistical Machine Learning, Reinforcement Learning & Multi-armed Bandits, Causal Inference, Distributionally Robust & Stochastic Optimization.

  • For applications, my research seeks practical solutions by developing or leveraging state-of-the-art analytics methodologies to yield insights and new functionality, and to address real-world needs raised by different industries and healthcare institutions. My research problems are motivated by the practice and data of my collaborators in Mayo Clinic, Massachusetts General Hospital, Michigan Medicine, Kellogg Eye Center, and St. Joseph Hospital.

    • Applications: Big-Data Business Analytics, Healthcare Analytics and Operations, Personalized Medical Decision-Making, Service Operations Management.

  • Previously, I have also explored topics related to revenue management, dynamic pricing, logistics and supply chain management.

  • More information on my work can be found on my publication page and Google Scholar.

Selected Honors and Awards

  • Finalist, INFORMS Health Applications Society (HAS) Best Student Paper Competition, 2021.

    • For the paper: Contextual Learning with Online Convex Optimization: Theory and Applications to Chronic Diseases.

  • Finalist, INFORMS Decision Analysis Society (DAS) Best Student Paper Competition, 2020.

    • For the paper: Contextual Learning with Online Convex Optimization: Theory and Applications to Chronic Diseases.

  • Winner, Katta G. Murty Prize for Best Student Paper on Optimization, 2020.

    • For the paper: Contextual Learning with Online Convex Optimization: Theory and Applications to Chronic Diseases.

  • Winner, Richard C. Wilson Prize for Best Student Paper on Service Systems, 2019.

    • For the paper: Advance Online Scheduling with Overtime: a Primal-Dual Approach.

  • University of Michigan Rackham Pre-doctoral Fellowship Award, 2019.

  • Winner, IOE Bonder Fellowship Award in Applied Operations Research, 2017.

    • For the paper: Coordinated and Priority-based Surgical Care: An Integrated Distributionally Robust Stochastic Optimization Approach.

Selected Professional Service

  • Journal Referee for Operations Research, Production & Operations Management, Naval Research Logistics, IISE Transactions, European Journal of Operational Research, Optimization Letters, etc.

  • Judge for MSOM Service Operations SIG Conference 2021.