Val Booth

About:

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 or Apollo. Previously at Intuit I worked almost entirely in Java, I worked in Backbone.Marionette and React JavaScript codebases at Hudl, I developed C code for GE Aviation, and I worked in C#, Python and Django for projects as a part of my class work.

Feel free to check out my contact information and expand my project sections (by clicking the double down arrow) below.

Contact:

• Email me at valxbooth at gmail.com
• Connect with me on LinkedIn.
• Follow me on GitHub.

Projects:

• Personal Projects
  • Mezcladorme 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 other audio features. Originally written in JavaScript for BrickHack 2016. View the source code here on GitHub.
  • 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 GitHub.