Having recently completed my Master’s in Computer Science, I blend academic research with hands-on software development, data analysis, and machine learning experience. I am currently seeking full-time early-career roles where I can build impactful, scalable solutions.
Marsh creation is one of the most cost-intensive and widely implemented restoration strategies in coastal Louisiana, yet significant uncertainties remain in predicting how hydraulically placed slurry will settle and consolidate. These uncertainties drive over- or under-runs in fill volumes and complicate construction monitoring. By improving field instrumentation and data interpretation, this project aims to strengthen design tools and reduce the risks associated with dredging operations.
This project is an automated architecture governance system designed to prevent "Architecture Drift" in scaling software projects. As development teams grow, established design patterns—often documented in "Architecture Manifestos"—frequently get overlooked due to review fatigue, stale documentation, or tight deadlines. This application utilizes an Agentic RAG (Retrieval-Augmented Generation) system to act as a real-time, automated mentor for engineering teams. It ingests local architectural guidelines and audits source code to ensure compliance with internal standards before code is merged.
LlamaIndex
ChromaDBDeveloped a dynamic Manga reader web application that leverages the MangaDex API to fetch and display manga-related data. Ensured the web application is fully responsive, adapting gracefully to various screen sizes and orientations to provide an enjoyable experience on both desktop and mobile devices.
Developed a mobile application in Flutter that allows users to scan grocery items adding them to their pantry stored in an SQFlite database. The app then uses the items to suggest cookable recipes.
Python program to extract data from excel sheets collected along the coastal areas of the Gulf of Mexico then using the data and machine learning the program tries to predict pockets of organic and inorganic material in the soil levels to test vulnerability to erosion.
A Python desktop GUI built for Civil and Environmental engineers to preprocess soil sample images using computer vision prior to quantitative analysis — replacing ad hoc manual steps with a repeatable, standardized workflow.