March 2023 - Present

View my work at DoorList to build out and scale the platform. Most of the work shown is frontend that you can see.

January 2023 - Present

Movie rating web app using the TMDB database, currently a work in progress.

December 2022

Created a web application using the Washington Metropolitan Area Transit Authority (WMATA) Real-Time Rail Prediction, Station List, and Rail Service Incident API to track rail service in real-time. Arriving trains can be filtered by station and display the line as well as its estimated arrival time. A map and real-time service diruptions are also availble within the application.

December 2022

The website you are on now. Made from scratch with love.

November - December 2022

Using two datasets modeled after different DDoS attacks, a python program was developed to detect if a DDoS attack is present within a PCAP file. Six different DDoS attack types were implemented and tested on a dozen different PCAP files.

Novemeber - December 2022

Using a dataset centered around The Beatles discography on Spotify, interactive charts were constructed and deployed as a Shiny app. The interactive charts pull from 7 of the 17 available variables in the dataset and allow the user to customize the data that is displayed.

August - December 2022

Using Django a web application was constructed enabling users to browse courses in the UVA course catalog and create schedules saved to their profiles. Course data was fetched from a UVA API. Users could create accounts using Google Sign on, find and make friends, view their friends schedules, and comment on their friends schedules. Data such as schedules, friends, and profiles were stored in a postgres database deployed on Heroku.

*Note the repo and site are private, due to UVA's policies. I am happy to chat about the project!

August 2019 - March 2020

Created a machine learning project using Keras with PlaidML in python; built a vision based convolutional neural network for image recognition.Built a network to identify and classify various road work signs vehicles would encounter in construction zones. Targeted towards vehicles which have begun the transition to full autonomy. Project development ended abruptly with an initial model created due to COVID-19 and school closure.

September 2016 - May 2017

In partnership with K2M, a scoliosis solutions company, prototyped a potential solution detecting early signs of scoliosis in patients using Google's Project Tango LIDAR solution and optical cameras. Designed an AI determining if a patient is showing signs of scoliosis earlier on than what can be seen by the human eye. Information would be used by doctors recommending the patient get an X-ray to get a more detailed look. Presented the solution to a panel of executives at K2M and won first place at the Innovation Challenge.