About

As a biophysicist, I am deeply interested in modeling the behavior of living systems across different scales and levels of abstraction. I believe the principles underlying the emergence of life also underlie the emergence of intelligent behavior, and neural systems are therefore an ideal setting for biophysics.

Geometry has been crucial in the development of modern theories of physics, from the symplectic geometry of Hamiltonian mechanics  to the pseudo-Riemannian geometry of general relativity. I expect that geometry will likewise play a fundamental role in our description of biological systems. Biology has long emphasized the idea that structure determines function: there exists a duality between the structure of a biological entity and the functions it carries out. 

After graduating from MIT in 2021, I began my PhD studies in the physics department at UC Santa Barbara. I am now working with Prof. Nina Miolane at the Geometric Intelligence Lab, where we leverage techniques from differential geometry and the cutting edge of computational statistics to explore the geometric signatures of natural and artificial intelligence. 

I am particularly interested in the geometric structure of neural representations -- the patterns of neural activity formed by the collective dynamics of neurons. My goal is to understand how the geometric structure of these neural representations plays a role in information processing in neural systems.