My research falls at the intersection of applied mathematics, statistics, and probability focusing in particular on the fundamental aspects of data science and uncertainty quantification as well as their applications in engineering and sciences. You can find more about my work in each of these areas using the following links:
- Machine learning : My work is focused on the analysis and development of algorithms for extraction of information from large datasets.
- Uncertainty Quantification: I am interested in rigorous analysis of Bayesian methods and more broadly UQ on infinite-dimensional spaces and the design of consistent algorithms for solving such problems.
- Applications: I have collaborations in environmental sciences, medical imaging and modeling of social and power networks.