Welcome! I'm

I build software that automates the mechanical parts of information processing so that scarce human attention goes where it is most needed. Learn more about Functionary, Lysilogy, Switchboard, and my other projects below. I also moonlight as an interdisciplinary researcher on the possibilities, limits, and dangers of optimization at Quasioptimal.

I'm looking for work in the San Francisco Bay Area. Reach out at the links below!

Trajectory
Home

Trajectory

Started in economics research, but learned along the way that I prefer building the tools and infrastructure that support research and knowledge work.

2020
2022
2024
2026
COURSEWORK RESEARCH DATA SOFTWARE
RESEARCH
DATA
SOFTWARE
Honors Thesis
Quasioptimal — Independent Research · 2023–Present 2023 – Present Interdisciplinary writing on the possibilities, limits, and dangers of optimization. I began gathering source material in 2023. Work ongoing at https://quasioptimal.io.

Current Work

AI Engineering Systems around models rather than models themselves: agents run behind review gates with a provenance record, every running session stays in view, and a model's claim is shown only where its source backs it.

Software Tools Tools for my own daily use: a plain-text task list driven from Neovim and a phone, Obsidian plugins that put a vim-style keyboard on a calendar and a map, an e-ink dashboard, and a cold-storage daemon for the home server.

Down the Tech Stack Down the Tech Stack To do the research backing the SSPI at IRLE, I had to become adept at working with data, both at the analytical level (competence in econometrics, statistics, and data science) and, as the data got bigger and harder to handle, at the infrastructure level. The process our team inherited was crude, manual, error-prone, and costly to reproduce. The primary obstacle to better research was scaling our data (covering more years and more countries, with better robustness checks and more statistical power), and the obstacle to scaling the data was engineering a reliable, reproducible collection process, so I taught myself to be an engineer.

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Skills
Trajectory

Skills

Technology
  • Systems & Backend: Design and implementation of correct, reliable, and performant systems in Rust and Python. Familiarity with Go, Typescript, and C.
  • Data: ETL Pipelines, DAG Workflows, Data Modeling, and Data Reliability. Experience with persistence layers in SQLite, Postgres, and MongoDB.
  • Web: Interfaces and webpages in TypeScript, JavaScript, React, and HTML/CSS.
  • Applied AI: Evaluations, observability, context engineering/RAG.
  • Infrastructure & Automation: Provisioning and operating of self-hosted and cloud-hosted services in Linux. Bash, CI/CD, Docker, and reproducible system configuration.
Technical Background
  • Mathematics: Fluency and rigor across linear algebra, probability theory, algorithms, real analysis, complex analysis, and convex optimization.
  • Data Analysis and Econometrics: Research and research design for regression analysis, both correlational and causal. Data analysis in Python, R, and Stata.
  • AI/ML: Fundamentals of AI theory—LLM architecture, scaling laws, reinforcement learning—and basics of model training in Python with PyTorch and NumPy.
People
  • Communication: Clarity, precision, and flair in technical writing and speaking.
  • Project Management: Coordination, delegation, and prioritization.
  • Technical Mentoring: Helping build conceptual understanding & problem-solving skills.
Portfolio
Skills

Portfolio