(1) analyzing geometries underlying neural or feature representations, embedding and transferring information, and (2) building neural network models and learning rules guided by neuroscience. To do this, we combine computational tools from theoretical physics, applied math, and machine learning. Alongside this theoretical work, we develop close collaborations with experimentalists to be inspired by and to test ideas on neural data.
NeuroAI & Geometric Data Analysis Lab
Animals can recognize latent structures in their environment and apply this information to efficiently navigate the world. Several works argue…
Nature NeuroscienceThe global dimensionality of a neural representation manifold provides rich insight into the computational process underlying both artificial and biological…
arXiv:2509.26560The human auditory cortex is topographically organized. Neurons with similar response properties are spatially clustered, forming smooth maps for acoustic…
arXiv:2509.24039
