My name is Parshan Pakiman. I am a Principal Researcher at the
Healthcare Initiative
of the University of Chicago Booth School of Business,
working with Professor
Dan Adelman.
I obtained my Ph.D. in Information and Decision Sciences from the University
of Illinois Chicago, where I had the privilege to work with Professor
Selva Nadarajah.
I obtained my B.Sc. in Applied Mathematics from the University of Tehran.
I will join the
Department of Operations Management and Strategy
within the University at Buffalo (SUNY) as an Assistant Professor of Operations Management.
Research. My cross-disciplinary research combines approximate dynamic programming,
data-driven optimization, and machine learning to deliver timely solutions to real-world business challenges
in the rapidly evolving age of AI and Data Science.
My research advances dynamic decision-making on two fronts:
(1) Specialized algorithms tailored to business applications. I investigate emerging healthcare and retail operations problems in which real-world business constraints—identified through collaboration with hospitals and industry partners—limit the applicability of existing methods. I thus develop algorithms with strong theoretical guarantees to tackle these impactful problems.
(2) Accessible optimization methodologies for business users. I develop algorithms to tackle large-scale Markov decision processes, specifically designing them to require minimal human intervention during implementation while ensuring near-optimal policies. My research broadens and eases the use of approximate dynamic programming for business users, even those with limited domain knowledge.
For a complete list of my published and working papers, please visit my research page.