High impact and versatile software engineer who strives to take initiative and deliver consistently high quality
results. I am seeking to broaden my current skillset of mainly backend and database development, to achieve a more
holistic vision of how technology pairs with the human world. I thrive in collaborative environments that reward
discovering areas of improvements and helping organize and develop solutions. In a previous role, I have managed to
devise and implement systems to reduce manual processes coordinating hundreds of thousands of community members
to contribute to specific datasets that test and improve some of the world's leading tech innovators.
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
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.
