Research
My background is in scientific computing, applied mathematics, uncertainty quantification, and data assimilation, with a focus on algorithms for modeling and simulation of multiscale, real-world processes. Accurate numerical simulations require (1) robust numerical algorithms and (2) knowledge of the true modeled system (initial conditions, uncertain parameters, and model error).
My current research interests are in scientific computing and scientific machine learning (SciML). I am also actively developing AI-based agentic workflows.
See the Group page for my current and former group members.
Research areas
Scientific machine learning
Hybrid physics/ML methods for modeling, inference, and uncertainty quantification.
Uncertainty quantification & data assimilation
Inverse problems, sensitivity analysis, and data assimilation for large-scale models.
Time integration
Robust time-stepping for stiff and multiscale dynamical systems (IMEX, multirate, adjoints), with methods implemented in PETSc and DESolve.
PDE & AMR
High-fidelity PDE simulation with adaptive mesh refinement and scalable solvers.
Project pages
See the Projects page for high-level research-thrust overviews, including Hybrid ML-PDE for accelerated simulation.
Software
-
DESolve (time integration package): https://emconsta.github.io/desolve GitHub: https://github.com/emconsta/desolve - PETSc time stepping: https://petsc.org/release/
- DAPack (data assimilation package): https://bitbucket.org/emconsta/dapack
- UQGrid (power grid dynamics; contributor): https://github.com/dmaldona/uqgrid
Publications
See the Publications page for a full list.