S. Scott Collis Fellowship in Data Science
Alejandro is a S. Scott Collis Fellow in Data Science in the Quantitative Modeling & Software Engineering department at Sandia National Labs. His work focuses on nonlinear model reduction of multi-component systems, and he is broadly interested in developing modeling and simulation techniques at the intersection of data science/machine learning and classical numerical methods. He received his MA and PhD in Computational and Applied Mathematics from Rice University and a BS in Mathematics from University of Maryland, College Park. While a PhD student, he was a DoD National Defense Science and Engineering Graduate Fellow and completed a research internship at Lawrence Livermore National Laboratory and a data science internship at Microsoft.
- PhD in Computational and Applied Mathematics, Rice University, 2024
- MA in Computational and Applied Mathematics, Rice University, 2022
- BS in Mathematics, University of Maryland, College Park, 2019
- Reduced-order modeling
- Nonlinear approaches, i.e. autoencoder-based nonlinear-manifold ROMs and quadratic-manifold ROMs
- Kernel methods for model reduction
- Coupling methods for multi-physics/multi-component systems
- Scientific machine learning
