About

I'm a scientist and engineer working at the intersection of deep learning, theoretical neuroscience, and neurotechnology, with the goal of uncovering principles of neural representation in brains and machines. 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 spent time as an engineer at Intel AI, Intel Neuromorphic Computing Lab, and Science Corporation. I co-organize the NeurIPS Workshop on Symmetry and Geometry in Neural Representations, and the ICML Workshop on Topology, Algebra, and Geometry in Machine Learning. To hear more about my work, check out this recent episode of the TWiML AI Podcast: Why Deep Networks and Brains Learn Similar Features.

Outside of science, I DJ / produce experimental electronic music as Aion. I'm based in San Francisco, with frequent trips to NYC and Europe. Reach out if you’re interested in meeting up.

Research Scientists & Engineers in multi-modal deep learning and interpretability:
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