Six years making data systems reliable. Now learning to make AI systems safe.
That work looked like quantitative dashboards at Fidelity and, before that, ML pipelines and computer-vision MVPs. These days I'm studying evaluations, robustness, and interpretability, and building my own safety-adjacent tools to learn in public.
Selected work
AI Safety Regulation Map
solo · liveAn interactive tracker of AI-safety regulation and policy news across every country and all 50 U.S. states. A news feed refreshes daily; regulation entries are curated by hand so the map stays trustworthy rather than auto-scraped.
Ground Knowledge
solo · liveA tool for contested research questions that weighs independent evidence instead of raw citation counts. It surfaces false consensus and source overlap, so twenty papers re-analysing one dataset count as one root, not twenty.
Experience
- Rebuilt a legacy quantitative dashboard portal (Python, Angular, AWS) for 200+ users and 50+ concurrent, replacing a system with silent data-pull failures and no version control.
- Re-architected deployment to decouple dashboards from the core portal, allowing R, Python, and Angular dashboards to scale without adding load to the portal.
- Cut page load times by up to 80% and raised automated test coverage to 90%; built a shared Angular component library and migrated legacy R apps to Python + Angular.
- Built an MVP video classifier end to end with FastAPI, Streamlit, and Docker.
- Engineered Python applications for portfolio optimization, turning heavy quantitative algorithms into maintainable code and cutting Monte Carlo runtime by 70%.
- Designed ETL pipelines for large-scale data, improving accuracy and reliability downstream.
- Ran impact analyses for global partners that shaped educational strategy for Minerva Forum users.
- Built an internal Streamlit analytics app that cut report creation time by 50% and supported 10+ configurable visualization types; mentored interns in Python.
- Taught 30+ students Python, ML, and statistics with a full curriculum, holding a 5.0 rating.
- As a Python data analyst at LucidMove, improved data-retrieval efficiency by 90% with optimized web-scraping and built predictive models on complex datasets.
Toolkit & background
- languages
- PythonJavaScriptRCSQL
- ml & data
- NumPyPandasscikit-learnMatplotlibSeabornPlotlyStreamlit
- backend / web
- FastAPISQLAlchemyAngularVue.js
- infra
- DockerAWSAzureTerraformGitJenkinsOracle
- methods
- ETL designstatistical modelingMonte Carloperformance optimizationpredictive modeling
Writing
// No posts yet. I'm starting to write up what I'm learning in the AI-safety courses and building notes on the projects. First pieces will show up here.
Open to safety- and alignment-focused roles, remote-friendly.
If you're working on evaluations, robustness, interpretability, or governance tooling and could use an engineer who ships, I'd like to hear from you.