Work

Research Assistant

HPC
Performance Modeling
Machine Learning
Active Learning
Uncertainty Quantification

AI performance modeling with uncertainty quantification and active learning for scientific applications running on Tuolumne.

University of New Mexico | Jan. 2026 – Present | Albuquerque, NM

As a Research Assistant, I work on building machine learning performance models for scientific applications running on LLNL’s Tuolumne supercomputer.

What I’ve Done

  • Built machine learning models with PyTorch and XGBoost to predict scaling behavior of AMG2023 on Tuolumne.
  • Designed and integrated methods for uncertainty quantification, active learning, and modeling performance variation. This improved prediction reliability and accuracy, and reduced data collection costs.
  • Automated performance data collection for heterogeneous applications running on Tuolumne at Lawrence Livermore National Laboratory. Used the Flux scheduler, Spack, and DuckDB. Modified source code of applications to add Caliper instrumentation and capture process topology.

Technologies

Python, C/C++, PyTorch, XGBoost, Scikit-Learn, Polars, Caliper, Flux, Linux, DuckDB, Cray-MPICH (MPI), ROCm