About



I was born in the US, raised in Venezuela until I was five, then lived in Boston for thirteen years. I studied at Stanford and lived in the Bay Area for five and a half years. I’m now happily back in New England.
In college I majored in Symbolic Systems with a concentration in mathematical logic. My master’s was in AI, though I also studied operating systems, networking, databases, and programming language theory.
I work across statistical modeling, data engineering, distributed training infrastructure, and inference performance optimization. Most of my work is in Python and, more recently, Rust. I like making systems run quickly, simply, and resource efficiently. The best software doesn’t get in your way.
Outside of work: playing classical violin, skiing and hiking on the east coast, tinkering, and keeping an eye on my Siamese cat.
All words are my own.
Experience
- 2020–presentMachine Learning Engineer, AI Engineer, and Data Scientist
Education
- 2018–19Stanford University, MS Computer Science
- 2014–18Stanford University, BS Symbolic Systems
Open Source
- aethergraph — Billion-scale graph sampling for GNN training.
- ane-bridge-rs — Rust bindings for the Apple Neural Engine.
- ccaudit — Fast Claude Code log viewer for tokens, cost, and history.
- hasp — Workflow pin scanner and sandboxed GitHub Actions runner.
- pgbattery — Raft HA manager and TCP gateway for PostgreSQL.
- power-monitor — Apple Silicon power monitoring via IOKit, with TUI and menu bar app.
- sme-gemm — SME GEMM kernels for Apple M4+.