About

I am a Staff Machine Learning Engineer at Inari Agriculture, applying my skills in mathematical physics, machine learning, and software engineering to solve difficult problems across plant biology and genetics, protein modeling, and CRISPR-based genome editing with the hope of contributing to a more sustainable planet.

Before joining Inari 7 years ago, I obtained a PhD in physics from Northeastern University (2017) where I developed and deployed high-throughput computational and machine learning methods for string theory, helping usher a traditionally pen-and-paper field into the age of big data. I continue to maintain a large, open-source data resource serving the international String Theory community.

My interests center on how structure shapes and encodes information, giving rise to emergent, real-world behavior. In particular, this includes leveraging physical and mathematical symmetries to build more expressive and interpretable models of complex systems, most recently with applications in systems biology.

In my work, I strongly advocate for building and sharing interdisciplinary knowledge, adapting mature frameworks from one domain to emerging problems in another to drive innovation forward.

Education