(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
The 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:2509.24039A key problem in deep learning and computational neuroscience is relating the geometrical properties of neural representations to task performance.…
Physical Review E
