معرفی
Stan Dimitrov is a Professor in the Department of Management Sciences at the University of Waterloo, Canada, and Director of the Business Data Analytics Lab. He holds a PhD in Industrial and Operations Engineering from the University of Michigan (2010). His research focuses on the intersection of operations research and information systems, with expertise in sustainable operations management, business analytics, mechanism design, prediction markets, game theory, nonlinear optimization, and network design. He has secured funding from NSERC, SSHRC, Mitacs, and the University of Waterloo.
Education:
- PhD in Industrial and Operations Engineering, University of Michigan (2010)
- MEng in Industrial and Operations Engineering, University of Michigan (2006)
- BSc in Computer Science, University of Michigan (2004)
Research interests include addressing industry challenges in pricing, process improvement, and customer relationship management. His work spans theoretical contributions (e.g., game theory, optimization) and applied domains such as wildfire management, circular economy policies, and mental health impacts of digital financial tools. Recent studies analyze pandemic-driven consumer behavior shifts and the role of AI in dynamic pricing.
He has received prestigious awards including the 2021 Canadian Operational Research Society Eldon Gunn Service Award and the 2018 Faculty of Engineering Distinguished Performance Award. His teaching portfolio includes advanced courses on scheduling, game theory, and data analytics, with a focus on graduate and professional development programs.
Advising and grants: Dimitrov actively supervises graduate students and holds Sole-Supervisory Privilege Status at Waterloo. His funded projects investigate topics like wildfire budgeting, circular economy subsidies, and eco-innovation licensing.
Labs/Teams: Leads the Business Data Analytics Lab, which develops data-driven solutions for organizational challenges. Collaborates on interdisciplinary initiatives in sustainable operations and emergency network resilience.
