Software Development Intern at Pharamaceutical Data Services

I first optimized an hourly data update process for a client, building off of an existing codebase in Python and SQL, and following object-oriented programming principles. I developed efficient API queries and implemented batch updates that reduced runtime by 95%. Additionally, I minimized data redundancy by 90%, eliminated manual processes, and significantly improved client satisfaction. To ensure correctness and efficiency, I conducted automated unit testing before deploying to the production database.

Next, I designed an AI-assisted ETL pipeline in Python to standardize over 100 altered datasheets and insert them into a relational database.

Research Intern at BioRay Pharmaceuticals

I worked in an R & D lab focused on developing mRNA cancer treatments. To identify potential new gene targets, I developed a tool in R to analyze single-cell mRNA expression data, implementing a pipeline performing data normalization, PCA-based dimensionality reduction, UMAP clustering, cell malignancy estimation, and data visualization. Using this tool, I then processed data across 20 cancer types and identified three potential new gene targets.

Research Assistant at SDSU Research Foundation

Working in the Laboratory for Pathogenesis of Clinical Drug Resistance, I led project analysis investigating misunderstood mutations in tuberculosis, identifying and classifying affected genes using Python and Bash. This work revealed systemic mutations in "pseudogenes" believed to be responsible for multiple drug resistance. I cleaned and analyzed large datasets using pandas and matplotlib, built two phylogenetic trees, and used chi-squared tests to determine the statistical significance of observed mutations. Throughout the project, I presented weekly updates to a 16-person lab team, including four in-depth presentations lasting over two hours each, as well as two comprehensive literature reviews.