I am pursuing my Master's in computer science at Arizona State University and plan to graduate in Spring 2025. Throughout my journey at ASU, I completed my Bachelor's in computer science in Summer 2024 and have learned a lot through coursework and my Master's thesis.

During my master's thesis, I have worked on GPU-acceleration tasks and comparing GPU and CPU performance on a variety of different tasks including machine learning, service-oriented computing, and large-scale image processing. Large speedups were achieved with an average speedup of 42.39x in the image processing section. The main part of my thesis and the title includes Dynamic GPU Kernel Scaling in Service-Oriented Architectures. Adding intelligent scaling to GPU kernels allows for greater efficiency meaning faster throughput in high request loads and saving resources during low request loads. This was achieved by creating and destroying GPU kernels based on different measurables including request queue size, GPU utilization, GPU available memory, and any other measurable the user wants to use. I have already completed my thesis and received a pass from my thesis committee.

In Summer 2022 and 2023, I completed software engineering internships for The Home Depot. I got valuable experience making Golang APIs, uploading these APIs to cloud services, and securing these APIs using a token system. In 2022, I developed an API to authorize check payments making the system faster, more secure, and more resilient to upstream and downstream changes. In 2023, I developed another Golang API to store health and location data for IT field to track PINpads and decide which ones need to be replaced.

During the Summer of 2024, I completed a software engineering internship for Intel Corporation in Hillsboro, Oregon. During this internship, I was able to work on a computer vision project and optimize the deployment for lower computing power systems. I decoupled docker containers to minimize their size and allow for customizable deployment and created startup scripts for each docker container to control the memory usage inside each docker container by minimizing files, python packages, and more.