Our faculty, whose expertise in operations research includes subfields in optimization, such as continuous, discrete, and stochastic optimization, conduct research with important applications in healthcare, astronomy, vision, network modeling, defense systems, and scheduling. This diversity in research foci, involving both theory and application areas, involves undergraduate and graduate students.
Faculty and students in operations research and optimization benefit from affiliations and collaborations across the university, including with JHU’s Applied Physics Laboratory, the Institute for Computational Medicine (ICM), the JHU Systems Institute, and the JHU Algorithms and Complexity Group. These affiliations and associated collaborations make our group a great example of the truly interdisciplinary nature that is characteristic of Johns Hopkins University.
Primary Areas of Research
Operations research aims to provide a framework to model complex decision-making problems that arise in engineering, business and analytics, and the mathematical sciences, and investigates methods for analyzing and solving them. The most common solution techniques include mathematical optimization, simulation, queuing theory, Markov decision processes, and data analysis, all of which use mathematical models to describe the system.
Optimization focuses on finding the minimum (or maximum) value of an objective function subject to constraints that represent user preferences and/or limitations imposed by the nature of the question at hand. Research in optimization involves the analysis of such mathematical problems and the design of efficient algorithms for solving them. It is therefore no surprise that optimization, while integral to operations research, has become an indispensable tool in other areas such as statistics, machine learning, computer vision, and computational biology, just to name a few. Optimization technologies provide examples of how deep mathematical techniques help to provide concrete computational tools for solving a diverse suite of problems. Consequently, the knowledge and experience gained by students who study optimization will make them highly competitive in the job market.
Complete descriptions appear in the course catalog.
View the semester course schedule.
- EN.553.361 Intro to Optimization
- EN.553.362 Introduction to Optimization II
- EN.553.371 Cryptology and Coding
- EN.553.4/600 Mathematical Modeling and Consulting
- EN.553.4/653 Mathematical Game Theory
- EN.553.4/661 Optimization in Finance
- EN.553.4/663 Network Models in Operations Research
- EN.553.4/665 Introduction to Convexity
- EN.553.4/693 Mathematical Image Analysis
- EN.553.730 Statistical Theory
- EN.553.731 Statistical Theory II
- EN.553.761 Nonlinear Optimization I
- EN.553.762 Nonlinear Optimization II
- EN.553.764 Modeling, Simulation, and Monte Carlo
- EN.553.765 Convex Optimization
- EN.553.766 Combinatorial Optimization
- EN.553.792 Matrix Analysis and Linear Algebra
- EN.553.735 Topics in Statistical Pattern Recognition