Hi, I'm
lucas
gover
About Me
I’m a data analyst and researcher with a strong foundation in polling, data, technology, and the political sphere. I graduated from the University of Puget Sound with a double major in Computer Science and Politics & Government, and I’m passionate about how these fields intersect—especially in the service of progressive change. Throughout college, I built my leadership through community and teaching—representing my university in competitive programming competitions, serving as a Resident Assistant for multiple freshman communities, and working as a TA, tutor, and lead instructor.
During the 2024 election cycle, I worked at Clarity Campaign Labs, partnering with political campaigns, nonprofits, and advocacy groups to analyze polling data, identify shifts in public opinion, and support decision-making through dashboards, visualizations, and analysis. Whether I’m working with code, campaigns, or collaborators, I bring technical fluency, a clear analytical lens, and a deep belief in data as a tool for impact.
Things I've Written
Recent research has found that large language models consistently capture and replicate undesirable societal biases relating to race, religion, and gender. However, political bias is not well explored. This study investigates the political bias present in the state-of-the-art large language model GPT-3. To investigate political bias, I apply Natural Language Processing techniques to develop a political sentiment analysis model. Using this model, I analyze the ideological bias present in political essays written by GPT-3, finding that GPT-3 has a moderate left-leaning bias and tends to replicate the ideological bias of prompt text.
Some Cool Projects
Texas Legislature Data Pipeline
Model Evalation for Political Campaigns
American Values Regression Tree
Website which visualizes how personal identities can shape political values. Users have the option to select which values and identities they are interested in. These selections are sent to a predictive model to better understand the relationships between them. The website renders this complex data in an easily understandable way, developing an intuitive yet detailed platform that can be used by both laymen and seasoned political scientists.
Movie Recommender
This project was designed to generate a graph of movies from a movie review database using Graph Analysis Algorithms to find relationships between movies. I implemented Dijkstra's algorithm and the Floyd-Warshall algorithm to generate movie recommendations based on similarity scores. I created an efficient interface that allowed users to gain insights into the graph, navigating the similarities between movies. As part of this project, I wrote a visual representation of the graph with D3.js, which was designed to give users a visual understanding of the movie relationships.
Birdsong Deepfakes
Solar System Lighting Simulation
This JOGL model displays a three-dimensional solar system with lighting, shadows, and realistic textures. It is created using Phong Shading which gives it a more natural, realistic look. The orbits of the planets are represented around the sun, with the planets constantly spinning as they travel along their trajectories. The Solar Body textures, lighting, and size are described by the given .sol file, which can easily be varied.
Political Sentiment Analysis Research
My senior politics capstone research on political bias in Large Language Models. I built and implemented a sentiment analysis model to analyze the political bias of the large language model GPT-3. My research furthers understanding of the potential societal biases represented in large language models and provides a means of measuring and understanding of these biases.
Books!

Currently Reading
I have the bad habit of reading too many things at once. Here's what I'm currently reading
The Words that Remain by Stênio Gardel
Big Data and Social Science by Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, and Julia Lane
The Lie Detectives by Sasha Issenberg