Who We Are

Rethinking sight restoration through models, data, and lived experience.

We are an interdisciplinary group exploring the science of human, animal, and artificial vision. Our passion lies in understanding how vision works, and how it might be restored in people living with incurable blindness. We focus especially on the emerging field of bionic vision, where insights from neuroscience and engineering converge to inform next-generation neurotechnology.

At the heart of our lab is a diverse team that brings together computer science, psychology, and neuroscience. We are united by a fascination with visual perception and a commitment to research that bridges theory and application. Our work spans computational modeling, psychophysics, and machine learning; from studying how individuals with visual impairment perceive the world, to building biophysical models of brain activity, to using virtual and augmented reality as testbeds for new visual accessibility tools.

What sets our lab apart is our close collaboration with implant developers and bionic eye recipients. We aim to unify efforts across the field by creating open-source tools and standardized evaluation methods that can be used across devices and patient populations. Our goal is not only to advance scientific understanding, but to help reshape how vision restoration technologies are conceptualized, tested, and translated—ultimately supporting greater independence and quality of life for people with vision loss.

Award-Winning Publications

We propose a perceptual stimulus encoder based on convolutional neural networks that is trained in an end-to-end fashion to predict the electrode activation patterns required to produce a desired visual percept.

We combined deep learning-based scene simplification strategies with a psychophysically validated computational model of the retina to generate realistic predictions of simulated prosthetic vision.

In the Spotlight

We introduce two computational models designed to accurately predict phosphene fading and persistence under varying stimulus conditions, cross-validated on behavioral data reported by nine users of the Argus II Retinal Prosthesis System.

We present a series of analyses on the shared representations between evoked neural activity in the primary visual cortex of a blind human with an intracortical visual prosthesis, and latent visual representations computed in deep neural networks.

We present a systematic literature review of 227 publications from 106 different venues assessing the potential of XR technology to further visual accessibility.

In the News

What is the required stimulus to produce a desired percept? Here we frame this as an end-to-end optimization problem, where a deep neural network encoder is trained to invert a known, fixed forward model that approximates the underlying biological system.

We optimize electrode arrangement of epiretinal implants to maximize visual subfield coverage.

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