About Me

LinkedIn GitHub Resume

My Background

I’m an M.S. candidate in Computer Science at Georgia Tech, where I hold a 4.0 GPA and expect to graduate in December 2026. I earned a B.S. in Statistics and Economics, summa cum laude, from the University of South Carolina Honors College, also with a 4.0 GPA.

My work sits at the intersection of machine learning, data science, software development, and empirical research. I enjoy problems that require both careful analysis and solid implementation: designing experiments, comparing models, auditing results, and turning technical work into systems or explanations that other people can use.

Recent projects include a self-hosted Signal assistant with isolated per-chat memory and automatic summarization, an OpenAI-compatible multi-model LLM council, and graduate machine learning studies covering supervised learning, unsupervised learning, randomized optimization, and reinforcement learning. I also reimplemented and evaluated a published neural architecture for hospital readmission prediction, using repeated trials and statistical testing to distinguish reproducible findings from configuration-sensitive results.

Before graduate school, I worked as a data analyst intern at GreyNoise Intelligence, where I automated data-quality and reporting workflows and analyzed customer churn and lead conversion. As an undergraduate research assistant, I worked with economic, demographic, and public-health data on questions involving STEM-worker agglomeration, spatial segregation, and racial disparities in birth outcomes.

Areas of Focus

  • Machine Learning & Evaluation: Model selection, hyperparameter search, repeated experiments, statistical testing, learning curves, and performance/runtime tradeoffs.
  • Applied AI & LLM Systems: Local model deployment, multi-model orchestration, retrieval and web search, memory and summarization, privacy-aware logging, and API development.
  • Data Science & Research: Statistical analysis, causal inference, predictive modeling, reproducibility, and communicating technical findings to research and business audiences.

Technical Skills

  • Programming: Python, R, SQL; familiar with Java, Stata, and SAS
  • Machine Learning & Data: PyTorch, scikit-learn, TensorFlow, Hugging Face Transformers, pandas, NumPy, tidyverse; supervised and unsupervised learning, reinforcement learning, NLP, and causal inference
  • Development & Visualization: Git, Linux, Bash, Docker, FastAPI, Jupyter, Google Colab, Streamlit, Quarto, GitHub Actions, and LaTeX

Outside of Work

I enjoy playing guitar, backpacking, traveling, and studying languages. I’ve traveled to 32 countries and speak advanced Spanish, intermediate German, and basic French.

Countries I’ve visited: a growing map!