Mathematical Programming

The group focuses on large-scale optimization problems and their applications in machine learning and data science. These problems are often characterized by vast amounts of data potentially distributed over an extensive network with restricted access, a large number of parameters, and complex problem models involving non-smooth and non-convex loss functions. 

Our primary research topics include:

• Disciplined convex programming
• Non-convex optimization
• Stochastic optimization and variance reduction methods
• Distributed and federated optimization
• Monotone operator theory and variational inference
• Implicit regularization and theory of deep learning
• Optimization on manifolds
• Optimization with quantum computers