Anna Kochnev Goldstein

The paper measures retinal resistivity using electrical resistance tomography in rat retinas, comparing results from commercial and machine learning-based methods.

The paper develops a machine learning model to identify retinal ganglion cell activation pathways from visually evoked potentials, avoiding the need for toxic synaptic blockers.

This study shows that residual photoreceptors affect the response of a degenerate retina to electrical stimulation, with implications for improving subretinal prosthesis design.

This paper optimizes a 3D electrode design for a photovoltaic subretinal implant to improve visual acuity beyond current clinical devices.

This study uses machine learning to detect human stress from EM reflections, achieving over 90% accuracy without relying on traditional markers like GSR.

This study upgrades a subretinal photovoltaic implant in rats, improving visual acuity from 100m to 28m pixels.

This paper accelerates multi-electrode array simulations using sparse and low-rank matrix techniques, achieving up to 133x speedup with minimal error.