I'm a Machine Learning Engineer at SandboxAQ, where I've been building novel navigation systems that use Earth's magnetic field. Our work on AQNav was recognized as one of TIME Magazine's Best Inventions of 2024.

I graduated from Dartmouth College with a double major in Physics and Government. My work sits at the intersection of physics, machine learning, and policy — I'm passionate about developing AI systems grounded in physical principles and ensuring they're deployed responsibly.

As the founding ML engineer on the AQNav team, I built our machine learning stack and helped achieve state-of-the-art navigation results. I've worked on R&D contracts with the U.S. Air Force, Boeing, and Airbus, including a successful real-time demo on a C-17 cargo plane.

Beyond my work in navigation, I've published research on Dissipative Hamiltonian Neural Networks, contributed to policy papers on innovation and national security, and continue to explore how physics-informed ML can solve real-world problems.