Coding Languages:

Python | HTML / CSS | JavaScript | C | C++ | C# | Java | SQL

Profiencies:

macOS/Linux/Windows | MySQL | Data Structures and Algorithms | Machine Learning Models | Neural Networks | GitHub | Node.js | Software Architecture | Frontend Development | Backend Development | Statistical Data Analysis and Visualization | Git | RESTful APIs | Responsive Web Design | GUI Creation | Software Development Skills | User Experience Design | Information Technology

Projects:

1. Man vs. Machine - CS 453 (Data Mining) at the University of Oregon/
   
   Man vs. Machine was the name given to a song recommendation system built using two different machine learning approaches: K-Nearest Neighbors and Matrix Factorization. The name alludes to the differing nature between the two models, as the Matrix Factorization system used human-made Spotify playlists as the basis of recommendation, and K-Nearest Neighbors used built-in statistics about the songs themselves. I built the Matrix Factorization system and co-authored the paper.
   
   PDF: Man vs Machine


2. easyA - CS 422 (Software Methodologies) at the University of Oregon.
   
   easyA was designed to let students visualize the grade distribution for different classes across the UO. I built the web scraper and administrative tools responsible for updating the data used.
   
   Link: easyA


3. Dollarless Dining - CS 422 (Software Methodologies) at the University of Oregon.
   
   Dollarless Dining is a visualization and information tool for students at the UO to view upcoming free food resources and where they are found. I came up with the concept, built the web scraper, and developed admin tools for the updating of the food information.
   
   Link: Dollarless Dining


4. Entertain Eugene - CS 407 (Career Internships Seminar) at the University of Oregon.
   
   Entertain Eugene allows users to either enter an artist's name, select a venue, or select a date range, to view upcoming events in Eugene relating to their choice. This was a personal project I created to work with GUIs and web scraping.
   
   Link: Concert Scan

In August of 2024, I completed an 8-week summer research program (REU) at Georgetown University in Washington, DC. The REU focused on Election Misinformation. I worked with Dr. Lisa Singh and multiple members of the Georgetown faculty and researchers at the Massive Data Institute (MDI). Additionally, I worked with nine other students from various universities across the United States.

Link to REU page: Georgetown Site

Our work split us into smaller groups, and my group focused on misinformation-heavy topics on the platform X (formerly Twitter). In this group, we developed classic machine learning models and later developed neural networks to ready in X posts and binarily classify them for three themes: election integrity, Trump's charges, and immigration.

As an REU cohort, under the continued guidance of Dr. Singh, we have continued our work by contributing to a research paper showcasing our results and findings. This paper is currently under review.

I would like to sincerely thank Dr. Lisa Singh for the opportunity to conduct this research. Additionally, I want to thank everyone who helped throughout the program, including the grad students and researchers at MDI who gave their time and effort to help us succeed.