Graduate researcher with a passion for machine learning, statistical modeling, and building tools that make complex data accessible to everyone.
About me
I'm a second-year master's student in Applied Data Science, focused on the intersection of machine learning and real-world decision making. My coursework spans statistical learning, deep learning, and data engineering.
Before graduate school I worked as a business analyst, which gave me a deep appreciation for how data insights need to be communicated — not just computed — to drive change.
Outside of code, I enjoy trail running, cooking, and reading about the philosophy of science.
Selected work
A collection of academic and personal projects spanning machine learning, NLP, and data visualization.
Built a gradient boosting classifier on EHR data to identify patients at high risk of 30-day readmission, achieving 0.81 AUC with SHAP-based interpretability.
View on GitHub →Fine-tuned a BERT model to classify sentiment and extract key themes from municipal policy documents, visualized in an interactive D3.js dashboard.
View on GitHub →Developed SARIMA and LSTM models to forecast hourly electricity consumption across 12 regions, reducing MAPE by 18% vs. baseline persistence model.
View on GitHub →Capabilities
My technical toolkit spans the full data science lifecycle.
Languages & Frameworks
Machine Learning & Statistics
Data & Visualization
Cloud & DevOps
Get in touch
Open to research collaborations, internships, and full-time roles starting Summer 2025.
Currently completing my master's thesis on causal inference in observational health data. Always happy to chat about data science, research, or career opportunities.