This application was my personal project for the Spring of 2019. It was created to upload activities recorded by my Fitbit watch while on a cross country cycling trip, with an added quote that changed every day.

  • MERN stack web application which uploads Fitbit metadata to Strava to document my cycling trip from Seattle to DC
  • RESTful API which stores quotes so the app can assign a random motivational quote as the title of the activity
  • Deployed on an AWS EC2 instance as a subdomain of my website: quotes.pauldorsch.com
  • Detects duplicate quotes using Levenstein distance between the added quotes and the previous quotes
  • Ride was called Journey of Hope, and I rode with 100 men of Pi Kappa Phi and raised over $500,000 for people with disabilities

BakedIn is was my main project over the summer of 2017 when I was an intern for Cloudbakers. I was the sole programmer of the vast majority of the web application, and it was able to be put into production towards the end of the internship. BakedIn makes use of many services and seamlessly combines them into a portal for the sales team to use in order to view customer information and company metrics.

  • Django Web Framework with model-view-template architecture and relational database
  • Frontend base developed using HTML, CSS, and JQuery
  • Hosted by Google Cloud Platform and contains 10 cron jobs to keep the customer data updated
  • Collected data by querying 6 seperate APIs and matching customers together with string matching
  • Google+ authentication with whitelisted login domains
  • Uses frontend libraries like Bootstrap, Selectize.js, DataTables, Bootswatch, Chardin.js, and more

I worked in a group to create this city database project as a final project for an introduction to databases class during my sophomore year of college. We created a fully functional database by constructing an entity relationship diagram (ERD) and then a relational mapping for our SQL tables. The program allows the user to create a username and password, and login using these credentials. Then the user may search the database for a countries, cities, locations, or events based on a detailed criteria that the user may set. The results of the queries were then displayed on a table that allowed the user to click to bring up further information. The user could also write reviews on any of these things, and could view past reviews and edit them. My part in this project was to help create the ERD and relational mapping, and then write all the queries for the various criteria the user inputed. I then wrote the UI for the user and embedded the SQL to create the fully functioning search system. I also created the clickable tables with the results of the queries. This project gave me deeper understanding of SQL and Python and improved my group work and management skills.

I created this Astar search algorithm program for fun while taking AP computer science and later refined and polished it during my freshman year at Georgia Tech. It searches an 800 X 800 node area, one node per pixel, for a total of 640,000 nodes. The program also allows the user to draw their own paths with the left mouse click, and then set a destination for the red circle with the right click. The red ball will find the shortest path according to the Manhattan heuristic and travel around the obstacles to its destination. This self imposed project allowed me to push myself and turn an abstract idea into a fully functioning product, as well as learn pathfinding and graph traversal.

This cloud was a project created for a demonstration at the 2018 Google Next convention. It uses a variety of Google Cloud services to analyze Freshdesk tickets (after removing any personal information) and portray the tickets in a certain color depending on the sentiment of the ticket.

  • Runs sentiment analysis on helpdesk tickets or custom text and changes a LED strip a certain color based on sentiment
  • Employs Google Cloud Storage, Cloud Functions, Google Cloud NLP, and Philips Hue Remote API
  • Built a RESTful API to take a sentiment score and display it in CIE color space between red and green