Behind every efficient hospital, on-time flight, and well-run supply chain is a set of hard math problems — and OR is how we solve them. ISEN faculty use optimization, probability, simulation, and machine learning to help complex systems make smarter decisions, with real-world impact across energy, healthcare, transportation, supply chains, manufacturing, and public policy.
Mathematical Optimization & Algorithms
Faculty design the mathematical models and algorithms used to solve complex optimization problems — including problems that are large-scale, nonlinear, or involve many interacting variables. This work forms the foundation for improving efficiency in systems like networks, supply chains, and resource allocation.
Stochastic Systems, Simulation & Uncertainty
Many real systems involve unpredictability — fluctuating demand, random failures, variable wait times. Faculty develop probability-based and simulation models to help understand, predict, and manage systems affected by uncertainty and risk.
Data-Driven Operations Research & Learning
This combines machine learning with optimization to turn data into decisions — not just predicting outcomes, but recommending actions. Work here includes reinforcement learning, data-driven optimization, and other methods that use data to guide better decision-making.
Networks, Games & Distributed Decision-Making
Some systems involve multiple decision-makers whose choices affect one another. Faculty study how network structure and strategic interaction shape outcomes, drawing on game theory, multi-agent systems, and distributed computing.
