My research topics are: model order reduction, optimal control and data-driven methods.Model order reduction:

• Reduced order modeling (ROM) is a crucial aspect in scientific computing for large-scale dynamical systems. The main idea is to reduce the dimension of the problem by orthogonal projection into lower dimensional subspaces typically computed by a SVD (singular value decomposition). The dynamical systems is, then, projected by a Galerkin method. Thus, instead of solving high dimensional problem, we consider a low dimensional one which turns out to be more efficient.

• Optimal Control: Dynamic Programming is an essential tool for the control of nonlinear problems. We can obtain a feedback control by solving Hamilton-Jacobi-Bellman equations numerically. It is well-known that this approach suffers from the curse of the dimensionality. I develop algorithms to mitigate this issue.

• Data-driven: is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is the same concept as big data and machine learning and it is applied in many field of statistics, economy, engineering and many more.