Hello, I'm

Luke Lambert

I'm a Computer Science and Mathematics student with a focus in software engineering, AI, and machine learning. As an NCAA Division 1 athlete, I bring dedication and a commitment to excellence to both my academic and professional pursuits. I am always eager to explore new ideas and work on exciting projects that push boundaries in technology.

Experience

University of Tulsa

MSc Computer Science ‎‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎ ‎‎ ‎ ‎ ‎ ‎ ‎ ‎ (Expected Graduation: May 2026)

BS in Computer Science, GPA 3.96 ‎ ‎ ‎ ‎ ‎ ‎ ‎‎ ‎ ‎ ‎ ‎ ‎ ‎ (August 2021 - December 2024)

BS in Mathematics, GPA 4.0

Capital One

Software Engineering Intern (Summer 2024)

Developing generative AI solutions to efficiently process and interpret dispersed data sets using Python, Chroma, NumPy, Pandas, Jupyter notebooks, Matplotlib, Langchain, and Gradio.

Implementing Retrieval-Augmented Generation (RAG) with vector databases to supply the generative AI model with critical information, enhancing response accuracy and relevance.

USAA

Software Engineering Intern (Summer 2023)

Developed a status dashboard web application using React.js to monitor and visualize database inconsistencies

Built and integrated Java Spring Boot APIs to enable real-time data updates in the React web app

Tandy School of Computer Science

Discrete Math Grader (Fall 2023-Spring 2024)

Evaluated and graded assignments for 35 students, encompassing topics like truth tables, proofs, graph algorithms, and probability, ensuring accurate understanding and application of core concepts

Projects

Track Calculator

The Track Calculator Web App is a dedicated calculator application for track and field enthusiasts, enabling users to perform a wide range of calculations and conversions pertaining to track and field events.
Built a full-stack track calculator project, leveraging a Java Spring Boot backend and a user-friendly React.js web app frontend with Bootstrap for a responsive and visually appealing design.

www.trackcalculator.run GitHub Link

Flick Finder

Flick Finder is a movie recommendation web app leveraging statistical learning to give movie recommendations based on movies the user likes
Developed with a Python backend using Pandas and Flask, and a user-friendly frontend built with React.ts and Bootstrap for a seamless and responsive user experience.

GitHub Link YouTube Demo

Dominant Darts

The Darts Scoring App is a Swift application developed using SwiftUI, which efficiently tracks and displays scores for darts games
Features a smart number pad for streamlined input, comprehensive statistics tracking, and essential information like checkout options for game completion