About

I'm a scientist, engineer, and theorist working at the intersection of deep learning and computational neuroscience. My research examines how biological systems construct structured internal world models and aims to identify computational principles that can translate those mechanisms into artificial systems.  I received my PhD from UC Berkeley, in the Redwood Center for Theoretical Neuroscience, my postdoctoral training in the Geometric Intelligence Lab at UCSB, and I've conducted research at Intel AI, Intel Neuromorphic Computing Lab, and Science Corporation - bridging neuroscience theory, geometric deep learning, AI, hardware-constrained learning, and brain-computer interfaces.

To hear more about my work, check out this episode of the TWiML AI Podcast: Why Deep Networks and Brains Learn Similar Features.