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 Interests


1. Self-Adapting Approximate Dynamic Programming: My research advances frameworks for dynamic decision making under uncertainty that are accessible to non-expert users. Rather than requiring users to manually design models, tune parameters, and solve large-scale optimization problems through trial and error, these frameworks automatically design, compute, and enhance their own approximations and produce high-quality decisions. By self-adapting to each problem instance's data and using information revealed during computation, these methods substantially reduce deployment complexity while maintaining strong theoretical performance guarantees, making advanced decision-support tools more accessible to users with limited domain expertise.


2. AI-Optimization Hybrids for Modern Business Applications: My research investigates emerging operations management problems shaped by real-world operational complexities, including operating-room scheduling systems that account for evolving workforce composition, pricing and demand-learning mechanisms that maintain consistency across retail channels, and marketing campaigns that account for long-term and cross-store effects. To address these challenges, I develop tailored algorithms that integrate AI and data-driven optimization technologies to deliver high-quality decision support and generate managerial insights for these complex dynamic systems.


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