About Me

I am a graduate student at the University of New Mexico developing AI performance models to guide optimizing scientific applications.

Background

I'm a Computer Science master's student at the University of New Mexico, where I work as a Research Assistant developing domain-informed machine learning performance models for parallel applications running on leadership-class supercomputers at LLNL.

My research sits at the intersection of high-performance computing and machine learning, integrating uncertainty quantification and into performance models built with deep neural networks or gradient-boosted trees. I also build the data collection and preprocessing pipelines that feed these models.

Before focusing on HPC research, I spent over a year as a Robotics Research Faculty Assistant developing ROS2 and micro-ROS software for cooperative transportation between mobile manipulators. I also worked as a Software Developer Intern at Duke City Digital, where I handled full-stack development, AWS infrastructure, and CI/CD automation.

Education

M.S. Computer Science, University of New Mexico (In Progress)

GPA: 4.0 / 4.0

Coursework: Digital Image Processing, Experimental Methods

B.S. Computer Science, University of New Mexico (graduated Dec. 2025)

GPA: 3.93 / 4.0, summa cum laude

Coursework: Machine Learning, Deep Reinforcement Learning, Data Structures and Algorithms, Parallel Processing, Computer Architecture, and Operating Systems.

Skills

Languages: Python, C, C++, JavaScript

High-Performance Computing: MPI, Spack, Slurm, Flux, Linux, Caliper

Robotics: ROS2, micro-ROS, Gazebo

Data & ML: PyTorch, Scikit-Learn, Polars, Pandas, Matplotlib

Cloud & DevOps: AWS (EC2, VPC), Nginx, Docker, GitHub Actions

Version Control: Git, GitHub