ISEN is home to a nationally prominent Operations Research faculty group
distinguished by two IISE Fellows, two INFORMS Computing Society Prize recipients,
four NSF CAREER Awardees, and an AFOSR Young Investigator Program Awardee.
Leveraging this deep expertise, ISEN offers a specialized sequence of advanced OR
depth courses unique in the nation. This academic leadership translates directly into
high-impact publications, major externally funded research projects, global professional
leadership, and outstanding doctoral student placements.
Operations Research develops rigorous mathematical and computational methods for
making better decisions in complex systems. ISEN faculty conduct research in
mathematical optimization, stochastic systems, applied probability, simulation, machine
learning, algorithms, and decision-making under uncertainty and strategic interaction.
Our research addresses problems involving resource allocation, system design,
planning, scheduling, network optimization, risk, congestion, reliability, and operational
decision-making. Faculty develop models and algorithms for continuous, discrete,
combinatorial, nonlinear, global, stochastic, robust, and large-scale optimization
problems, as well as probabilistic and simulation models for systems affected by
uncertainty, variability, and interdependence.
Applications span energy, healthcare, transportation and logistics, supply chains,
manufacturing, service systems, communication networks, autonomous systems, public
policy, and other large-scale engineered and societal systems.
Mathematical Optimization & Algorithms
Develops mathematical models, algorithms, and computational methods for continuous, discrete, combinatorial, nonlinear, global, network, polynomial, stochastic, robust, and large-scale optimization problems. Representative areas: integer and combinatorial optimization; global/nonlinear optimization; network optimization; large-scale methods; decomposition and approximation algorithms.Stochastic Systems, Simulation & Uncertainty
Develops probabilistic, stochastic, and simulation-based models for systems affected by uncertainty, congestion, variability, and random events. Representative areas: queueing systems; applied probability; Markov models; stochastic modeling; simulation; simulation optimization; ranking and selection; risk, reliability, and sequential decision-making.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
Studies interconnected systems in which decisions, incentives, information, and performance are shaped by network structure and strategic interaction. Representative areas: graph and network optimization; network analysis; game theory; evolutionary games; multi-agent systems; distributed optimization; distributed computing and information processing.