Research Areas

Research in optimization ranges from the design and analysis of new algorithms to their software implementation. A vital area of applied optimization is the formulation of models that are both tractable and representative of real life applications.

Areas of emphasis are nonlinear, stochastic and discrete optimization, often in the context of machine learning applications. There is a strong methodological flavor to much of our research in optimization, which is motivated by areas as diverse as medical treatment, machine learning, security and energy systems.

Optimization problems arise in all areas of engineering and science. Most realistic models must deal with uncertainty in model parameters and data, and one of the main open problems is how to perform the optimization in large scale setting.

Learn more about optimization 

Course Work

The study of optimization focuses on:

  • Fundamentals in analysis and linear algebra
  • Investigation of many algorithms and the diverse formulations
  • Services and languages for communicating between formulations and algorithms
  • Researching specializations to particular application domains


Faculty members in optimization include: