Sajad Modaresi uses analytical modeling and statistical and machine-learning tools to provide insights into operational decision problems and design implementable solutions. His research mainly focuses on data analytics in retail operations, online personalization, and data-driven approaches to decision-making under uncertainty. He is particularly interested in settings where estimation and optimization are integrated to make dynamic operational decisions.
His teaching interests are business analytics, retail operations, data-driven decision-making and operations management.
His article “A Dynamic Clustering Approach to Data-Driven Assortment Personalization” is forthcoming at Management Science. He and his co-authors received second prize in the 2013 INFORMS JFIG paper competition for their paper “Learning in Combinatorial Optimization: What and How to Explore.”
Dr. Modaresi received his PhD in operations management from the Fuqua School of Business at Duke University. He received his master of science degree in industrial engineering from the University of Pittsburgh and his bachelor of science in industrial engineering from Sharif University of Technology.