I've trained as a physicist and applied myself to quantum computing, a field where I frequently moved across hardware, software, and science and learned adjacent disciplines on the job.

That work led naturally into AI systems for research: agentic tools and harnesses, surrogate models, and cloud-backed workflows that make difficult technical work like building a quantum computer more tractable.

Currently based in the Los Angeles area.

Agent tools: Claude Code, Codex, Pi. Frameworks: PyTorch, JAX, OpenMDAO. Languages/platforms: Python, Julia, AWS.

EXPERIENCE

AMAZON WEB SERVICES

Sr. Applied Scientist

Jan 2023 – Present

Quantum Infrastructure Manager

Aug 2021 – Dec 2022

Applied Scientist

Feb 2020 – Aug 2021

EDUCATION

CALTECH

July 2015 - Jan 2020

Ph.D. in Applied Physics. Yariv-Blauvelt Fellowship. Advised by Oskar Painter. Built out a new part of the lab, designed and fabricated the first qubit, explored a high risk high reward fabrication method to make qubits more coherent when integrated with a transducer.

Thesis: Suspended Trace Air-Gap Resonators for Low Loss Superconducting Circuits

UC SANTA BARBARA

Aug 2011 - Jun 2015

B.S. in Physics and Applied Mathematics. Physics Academic Honors. Research Honors. Academic Excellence Award. Advised by John Martinis. Designed and made impedance matched infrared filters, cryogenic parts for Google Quantum AI's first custom dry fridge, and ran simulations for a tunable qubit coupler.

Thesis: Development of Hardware for Scaling Up Superconducting Qubits and Simulation of Quantum Chaos