Master's Computer Science

Ukiah Sperry

***********@*****.*** • 828-***-**** • www.linkedin.com/in/ukiah-sperry •

https://github.com/ukiah-sperry • https://main--ukiahsperry.netlify.app/ EDUCATION

The University of North Carolina at Charlotte Charlotte, NC M.S. in Computer Science May 2026

B.S. in Computer Science GPA: 3.6/4.0 May 2025 Organizations: Formula SAE, Honors Thesis in Reinforcement Learning, Truist Accelerator program

TECHNICAL SKILLS

Technologies: Python, Flask, Jupyter Notebook, Numpy, Seaborn, MatPlotLib, Pandas, Scikit-learn, React.JS, Javascript, OpenCV, Java, C++, C#, Git, PostgreSQL, HTML, CSS

WORK EXPERIENCE

UNCC CCI Lead Teaching Assistant Charlotte, NC Aug 2023 - Present

● Helped 300+ students in Intro to Python and Intro to Java improve their programming skills, by hosting comprehensive class sessions and providing extra videos and readings

● Streamlined assignment grading processes, reducing grading time by 30%

● Organized weekly office hours attended by an average of 25 students/week, with a 95% satisfaction rate

Starship Robot Technician Charlotte, NC March 2022 - Aug 2024

● Managed a fleet of 40 autonomous delivery robots, maintaining a 98% operational uptime through efficient repairs and proactive maintenance

● Completed over 350 robot repair tickets, resolving 90% on the first attempt, minimizing delivery delays, by effectively utilizing Jira and Confluence to track and prioritize tasks

● Streamlined parts management process by keeping a meticulously organized inventory, achieving 90% parts availability and reducing retrieval times by 30%, compared to other hubs, PROJECTS

React Jobs Page

● Developed a dynamic jobs page application enabling users to delete, add, edit, and view jobs, leveraging a JSON server to manage data storage and retrieval

● Built user interfaces using React and JavaScript (ES6+)

● Optimized development workflow with Vite, reducing build times

● Styled the application with PostCSS and Tailwind CSS, ensuring consistent and modern UI Vehicle Price Prediction

● Built Neural Network & KNN models for car price prediction with 94.7% R accuracy with KNN and 92.35% with NN

● Processed 15,915 car listings, applying feature scaling and one-hot encoding.

● Used PyTorch & scikit-learn for model development and evaluation.

● Visualized insights with Matplotlib & Seaborn to analyze price factors.

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