Research 

Research Interests

My research centers on the intersection of numerical analysis, applied mathematics, and computational statistics, with a particular emphasis on inverse problems. I am interested in the development, analysis, and implementation of stochastic and data-driven algorithms for the efficient solution of inverse problems arising across diverse areas of mathematics. This includes designing robust computational methods that address issues such as ill-posedness, uncertainty quantification, and high-dimensional inference, while leveraging modern statistical, data-driven and graph based techniques to extract meaningful information from complex data.

More specifically, my research is conducted through the following areas:

1. Inverse Problems 

2. Regularization Theory

3. Graph Laplacian Regularization

4. Stochastic Gradient Descent 

5. Data-Driven Methods

6. Stochastic Optimization

7. Statistical Inverse Problems

8. Mathematical Imaging

9. A priori, A posteriori and heuristic stopping analysis