I’m a research scientist working on theoretical neuroscience & deep learning
I'm interested in building interpretable AI systems and uncovering principles of neural representation in brains and machines. Some topics I've worked on include: geometric/topological deep learning, deep generative models, mechanistic interpretability, symmetry discovery, and neural population decoding.
I received my PhD from UC Berkeley in 2021, under the mentorship of Bruno Olshausen in the Redwood Center for Theoretical Neuroscience. I’m currently appointed as a Postdoctoral Scholar in the Department of Electrical and Computer Engineering at UC Santa Barbara, where I work with Nina Miolane in the Geometric Intelligence Lab. I have an interdisciplinary background that includes applied mathematics, linguistics, cognitive science, and philosophy, in addition to machine learning and neuroscience.
My research has been supported by the PIMS-Simons Postdoctoral Fellowship, the NSF GRFP, and the Beinecke Scholarship. I’ve worked as a researcher in Intel’s Neuromorphic Computing Lab and AI Division. 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.