Hello, my name is Val Booth. I'm a Software Engineer 2 in the QuickBooks Payments segment at Intuit Inc. in Mountain View and a 2017 Computer Science alumna from RIT. As a student I was a software engineering co-op at Intuit (in San Diego) and Hudl for 6 months each, and an engineering intern at GE Aviation.
Although I consider myself a full stack engineer, I've spent my full time career at Intuit so far working in React and related frameworks such as Redux
Feel free to check out my contact
information and expand my project sections (by clicking the double down arrow) below.
Email me at valxbooth at gmail.com
- Personal Projects
View Mezcladorme live here. Mezcladorme is a webapp based on
Spotify’s “Sort Your Music”
that loads a user’s Spotify playlists and rearranges songs by creating a minimum
spanning tree with edge weights based on song similarity. The similarity between songs is found by comparing beat, volume, and
- Modular Multiplicative Inverse Calculator
View the calculator live here. The modular multiplicative inverse calculator is a script to
calculate and show the equations used to find the GCD
then multiplicative inverse (if there is one) for a number in a modular
ring up to 50,000,000. Finding the modular multiplicative inverse is used in practical applications to
speed up the encryption process
of RSA. Thanks to
Adam McCarthy for helping test the script. View the source code here on GitHub.
- School Related Projects
- Intro to Data Mining Project: "Shall We Dance? Using Naive Bayes and Spotify Data to Predict If a Song is a Good Dance Song"
Fetched, cleaned, and ran Weka's data mining algorithms on Spotify's "audio features" for over 1,000 dance and other popular songs from multiple decades to determine how danceable a song was. Compared true positive and
AUC values for multiple algorithms (with differing sets of features) and then implemented and tested the Naiive Bayes model of the original data set. View some of the scripts and the writeup here on GitHub.
- Intro to Big Data Project
Developed a Python native app that allows the user to filter and make graphs of three years worth of FAFSA dependency filing data
representing every college/university in the US, along with outside territories. Data was retrieved from
data.gov and converted into a SQLite database before generating bar graphs with plotly
and being displayed in a PyQT GUI. Standalone Windows app was
created with PyInstaller. The project was developed by Team Honey Badgers, which included Nick Jenis
and Mark Petrie. Project source code can be viewed here on