Francisco Acosta

I am a physics PhD candidate at UC Santa Barbara working at the intersection of deep learning and computational neuroscience. I study how neural networks build internal representations that support computation.

My research uses dynamical systems theory, group theory, and geometry to understand emergent structure in deep learning systems. I am especially interested in how learned representations are shaped by the interplay between task structure, network architecture, and learning dynamics.

Before UCSB, I earned my S.B. in Physics from MIT in 2021. During my PhD, I have also worked with Atmo as an AI Researcher building deep learning models for large-scale atmospheric forecasting.

I also help organize NeurReps, the NeurIPS Workshop on Symmetry and Geometry in Neural Representations.