I grew up in the East Bay Area and attended UC Santa Barbara, earning degrees in Mathematics and Physics. My passion for coding began during college, where I discovered how my mathematical and problem-solving skills could be used to create practical tools.

In 2017-2018, I joined the Data Science Club, where I collaborated on the SpongeGuyParkFeld project. We used NLP and machine learning techniques to analyze TV show transcripts, which solidified my interest in data science. I contributed by scraping data, defining tasks, and helping the team achieve initial classification success, setting the stage for deeper analysis.

In my first ever engineering internship at FreeWire Technologies, I was tasked with developing a touch screen user interface for their Mobi L2/Gen series. In that role I had to diligently listen to and learn from my teammates and mentors, as well as improve as a software developer. I learned how to write robust and maintainable code by making mistakes and working through inefficiencies, having to work off-the-clock to meet my goals for the company. This experience working alongside engineers in a startup was truly invaluable for me.

Upon graduating from UCSB, I began working for FLIR Systems (now Teledyne FLIR LLC) as a Software Engineer, where I learned even more valuable skills related to validating software, automating test cases, and designing software architecture. I learned how to build and publish my own Python packages, implement fixtures and plugins for our test automation framework, write robust unit tests, and quickly debug code, among many other things. Outside of really growing as a Python programmer, I gained exposure to the C programming language, learning the cameras' signal processing pipeline architecture, compiling and wrapping C SDKs to talk to the cameras, and successfully finding algorithmic bugs in their embedded software down to specific lines of code.

After moving back to the Bay Area and working remotely for FLIR for one more year, my time at FLIR ended in 2023. After exploring my options, I decided to delve more deeply into the field of data science, specifically machine learning engineering, to better understand the field as it is growing as a profession. I enrolled in the November 2023 cohort of the UC San Diego Extended Studies MLE Bootcamp and embarked on my learning. As I got deeper into the curriculum, I found myself getting more and more excited about testing out different machine learning architectures, tools and libraries, experimenting with feature engineering and data pre-processing, model tuning, deployment, and exploratory data analysis. The more hands-on I got with the assignments, the more fun I was having, and my favorite part was getting to work on my capstone project, LIRecommend, an instance of a machine learning powered job recommendation system trained specifically on my responses to job postings. To learn more about this project, see LIRecommend repo.