My favorite project! built a web application that lets developers chat directly with GitHub repositories using Retrieval-Augmented Generation (RAG). Simply share a GitHub repo link, and the AI analyzes the entire codebase to answer any questions about the code structure, functionality, or implementation details. What made this project exciting was creating something I actually needed, saving me time when exploring new codebases or revisiting old projects. Instead of digging through multiple files, you can just ask questions and get informed answers based on the actual code.

During my research at Iowa State University, I worked on using AI to detect plant stress - basically teaching computers to spot when crops aren't feeling their best. I evaluated Vision-Language Models while working alongside PhD students and faculty, analyzing how well these models could understand and describe plant health issues. My favorite part was explaining my work to professors or curious kids during the symposium at the end of the research, making AI accessible and exciting to everyone. This showed me that sometimes the biggest challenge isn't just building the tech but helping others understand and get excited about it too. Available on Google Scholar!

This flashcard generation system leverages OpenAI API to transform text into interactive study materials. Working with a team, as part of my Headstarter internship, we combined our expertise in frontend and backend development to create an intuitive learning tool. The project strengthened both my technical skills and team collaboration abilities, as we worked together to solve challenges and implement smooth user experiences. It was particularly rewarding to see how our different perspectives and skills came together to create a tool that helps people learn more effectively.