I am interested in exploring patterns and structure in complex systems—whether social, linguistic, biological, or computational. Since mathematics is the study of structures and patterns, it’s generally the best tool for describing those systems (and also has aesthetic value as its own discipline).

I have a bachelor’s degree in Mathematics and minors in Linguistics, Computer Science, and Formal Logic.

My early on-the-job training came while working under software engineers, computer science professors, and statisticians. This led to my proficiency with object-oriented programming in C++, Python, and Java; data manipulation and analysis in R and PostgreSQL; and machine learning techniques with PyTorch, scikit-learn, and OpenNMT. My areas of academic research included phonetic transliterations of historical English surnames, applications of large-scale language models (e.g. GPT-2) to procedural generation of NPC dialogue in video games, experimenting with procedural music generation (both MIDI and audio), and studying the effects of tagging information about language families on encoder-decoder multilingual neural machine translation models.

Most recently, I’ve been working at Meta Reality Labs as an AI Software Engineer. I’ve been focused on designing, building, and maintaining agentic frameworks to automate a variety of QA tasks including bug reproduction, fix verification, and end-to-end testing. I’ve specifically worked on MCP server development, evals, and surrounding infrastructure.

I was previously employed at Pacific Northwest National Laboratory as a research associate working primarily across two projects. The first project involved data and software engineering for a framework of graph analytic pattern-matching algorithms (i.e., subgraph isomorphism methods) for anomalous pattern-of-life detection. I played a variety of roles on the project, assisting with data curation, ETL pipeline development, and front-end visualization. For the second, I leveraged LLMs, RAG, and clustering algorithms to improve graph-based models of threat patterns in cyber-physical systems.