Research Focus
Bayesian inference, uncertainty quantification, data assimilation, generative modeling, scientific machine learning, mathematics of data science.
I make use of geometry and structure to develop principled computational methods for probabilistic modeling and inference, encompassing Bayesian inference, generative modeling, and data science. My current focus is on effective design and structure exploitation within measure transport approaches for sampling, data assimilation, and stochastic inverse problems.
Publications
- A. Maurais, T. Alsup, B. Peherstorfer, and Y. M. Marzouk. Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices. SIAM Journal on Mathematics of Data Science, 7(1):189–223, Mar. 2025
- A. Maurais and Y. Marzouk. Sampling in Unit Time with Kernel Fisher-Rao Flow. In Proceedings of the 41st International Conference on Machine Learning (ICML), pages 35138–35162. PMLR, July 2024
- A. Maurais, T. Alsup, B. Peherstorfer, and Y. Marzouk. Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry. In Proceedings of the 40th International Conference on Machine Learning (ICML), pages 24214–24235. PMLR, July 2023