about

about : {

objective :
I enjoy making things work. To accomplish this simple objective I strive to be a highly collaborative individual, develop deep knowledge of systems and processes as a whole, and identify solutions using modern tools and frameworks as they evolve within the world of software

industry : {
discipline : software developer
speciality : fullstack
Education : B.S. in Computer Science, Oregon State University

}

skills : {

programming Languages : [
Python, JavaScript, TypeScript, C/C++, HTML/CSS, SQL, Ruby, PHP
]

code management tools : [
Git, Docker,, Rancher, Jira, AWS CLI,
]

concepts && frameworks : [
CI/CD, Nuxt, Vite, React, NoSQL
]

}
hobbies : [
skiing, hiking, climbing, cooking, hot sauce,
]
}

projects

Applause

Developed web applications and various data collection/transformation services for the Applause AI/ML team.

To learn more specifics about what Applause does, you can find more information on their website here: applause.com

Hearing Simulator

The mission of this project is to offer a free and open source web application for conceptualizing hearing loss through live audio manipulation.
The audio manipulation interface allows the user to interact with a replication of an audiogram; a chart used by audiologists to display the results of a hearing test.

To serve this app I used the React framework and managed state using Redux, which allowed for smooth handling of the Web Audio API library I choose to use Freesound.org's API for users to query and select from a large range of user uploaded sounds.

Working on this project has been a rewarding experience for me and I look forward to developing more technology engaging with the human experience.
If you have any questions or comments about this project the best way to reach me is through my email linked at the top of this page

Concert Program OCR

The Concert Program OCR (Optical Character Recognition) project aims to provide machine learning researchers with a robust database of metadata, program images, OCR text, and supplementary information for classical music concerts.

I personally worked on the backend section of this project. First I collected data using a web scraping script on existing digital archives. Then I was able to run an Open Database Connection (ODBC) and pipe SQL queries with the collected data directily into a Mircorosft SQL Database hosted on a Docker container.

With that same ODBC connection established I could access and hand off the data in various formats to the front end team, who used Streamlit to allow filtring and downloading of the information to be loaded into dataframes for machine learning algorithms.