University at Buffalo
My name is Parshan Pakiman. I am a tenure-track Assistant Professor of Operations Management at the University at Buffalo (UB) School of Management, affiliated with the Department of Operations Management and Strategy.
University of Chicago Booth
Before joining UB, I was a Principal Researcher at the Tolan Center for Healthcare at the University of Chicago Booth School of Business, where I had the great opportunity to work with Professor Dan Adelman.
University of Illinois Chicago
I earned my Ph.D. in Information and Decision Sciences from the University of Illinois Chicago, an experience I am deeply grateful for. I had the honor of being advised by Professor Selva Nadarajah and worked closely with Professor Negar Soheili.
University of Tehran
I obtained a B.Sc. in Applied Mathematics from the School of Mathematics, Statistics, and Computer Science at the University of Tehran.

Research

My research focuses on helping firms make better dynamic decisions under uncertainty, real-world business constraints, and limited data availability, challenges that often require the development of new optimization and learning methodologies. I work at the intersection of stochastic optimization, approximate dynamic programming, and data-driven modeling. In particular, my research advances solution methods for large-scale Markov decision processes arising in emerging operations management applications by combining (i) modern approximation techniques, (ii) optimization and AI technologies, and (iii) high-dimensional sampling tools. The development of these methods is motivated by problems in inventory management, healthcare operations, and revenue management.


A broader goal of my research is to make real-world sequential decision models easier for non-expert business users to formulate, solve, and deploy. Rather than relying heavily on hand-crafted approximations, extensive parameter tuning, or user-driven reformulations, I study frameworks that automate key parts of the modeling and solution process while preserving strong theoretical guarantees. In particular, these self-adaptive frameworks incorporate mechanisms that inherently exploit problem structure, instance-specific data, and information revealed during the solution process, rather than requiring users to exploit and encode such structure in advance.


For a complete list of my published and working papers, please visit my research page.