My Projects

This website was created with the Next.js framework, using MongoDB for database management, and hosted on Vercel. All of the code was written by Cursor AI over the span of a week but it is being actively updated and maintained by me. The website contains a homepage, about page, projects page, blog, reading list and contact form. There is also an admin only side, with functionality for the admin to update the blog, reading list and projects page on the client side, as well as a profile page which allows the admin to adjust their email and password. The link belows links to the github repository storing the code for the website.

In the Spring 2025 semester, I accepted a muser research project from Dr. Leonardo Ferriera and completed an independent study. The goal of the project was to identify and visualize differentially expressed proteins. The dataset contained four condition groups: wild type (WT) mice, knock out (KO) mice, and knock out mice with two treatment delivery methods, I.V. (KO-IV) and Nebulizer (KO-NEB). Each condition group had three samples, for a total of twelve samples. Differential expression was determined across three contrasts groups, where each contrast group was a comparison of either WT, KO-IV or KO-NEB to the baseline KO group. Using these three contrast groups, I implemented differential expression analysis and gene set enrichment analysis workflows in R to create visualizations such as multidimensional scaling plots, volcano plots and gene concept network plots. Below is a pdf of the finished report.

This screenplay was my final project for my screenwriting class, Writing the Short Film, with Professor Gary Hawkins. This is a short story about a father who tries to go in to work on a Saturday to close a deal with a business client, but keeps getting interrupted by ridiculous obstacles.

In this project for my computational genomics class, my partner and I used the keras core library to build neural networks for the binary classification of simulated gene sequence datasets. The challenge of the project was that each of the four simulated datasets were structurally different, so different models had to be developed for each dataset. Below are links to the pdf of the report, and a zip file containing the code.

I collaborated within a team of four to analyze a large, messy dataset and create data visualizations for an online interactive statistics textbook, CourseKata, used by high school students nationwide. Our team analyzed the effect of learning strategies (repetition, organization elaboration, imagery, and extraction) on student performance and participation. We did this by first grouping each chapter's interactive question types into the learning strategies that most represented them, then analyzed how well students did on the end-of-chapter questions to determine which interactive question types resulted in the most retention of information. We found that chapters with a larger ratio of multiple-choice questions had worse average end-of-chapter scores than chapters that better utilized other question types. We also found that students engaged with the material more (spent more time on the interactive textbook) when videos were included in the chapters. We utilized R studio to create these visualizations and presented our findings to a panel of industry experts and Duke faculty.