Fig. 1: Degenerative retinal diseases cause irreversible vision loss in more than 10 million people worldwide. Analogous to cochlear implants, retinal prostheses electrically stimulate surviving retinal cells in order to evoke neuronal responses that are inter-preted by the brain as visual percepts (‘phosphenes’).
The 'bionic eye'—so long a dream of the future—is finally becoming a reality with retinal prostheses available in the US and Europe (Fig. 1; over 300 patients implanted). With cortical implants, optogenetic approaches, and stem cell therapy in development, a wide range of sight recovery (SR) options will be available to patients suffering from severe blindness.
Despite the increasing clinical and commercial use of these devices, the perceptual experience of SR patients is surprisingly poorly understood. A common misconception in the field is that each electrode in an array can be thought of as a 'pixel' in an image; to generate a complex visual experience, one then simply needs to turn on the right combination of pixels. However, almost all SR technologies are likely to suffer from perceptual distortions and subsequent loss of information due to interactions between the technology and the underlying neurophysiology.
The goal of my research is therefore:
  1. to understand how interactions between SR technologies and neurophysiological mechanisms shape the visual perception of SR patients, and
  2. to use this knowledge to develop advanced stimulation strategies for different SR devices, with the ultimate goal of restoring useful vision to people suffering from severe blindness.

Selected Publications

More publications Google Scholar

In this review, we provide an accessible primer to modern modeling approaches and highlight recent data-driven discoveries in the domains of neuroimaging, single-neuron and neuronal population responses, and device neuroengineering. Further, we suggest that meaningful progress requires the community to tackle open challenges in the realms of model interpretability and generalizability, training pipelines of data-fluent human neuroscientists, and integrated consideration of data ethics.
Curr Op Neurobiol 58:21-29, 2019

Current retinal implant users report seeing distorted and often elongated shapes rather than small focal spots of light that match the shape of the implant electrodes. Here we show that the perceptual experience of retinal implant users can be accurately predicted using a computational model that simulates each individual patient’s retinal ganglion axon pathways. This opens up the possibility for future devices that incorporate stimulation strategies tailored to each individual patient’s retina.
SciRep 9(1):9199, 2019

The goal of this review is to summarize the vast basic science literature on developmental and adult cortical plasticity with an emphasis on how this literature might relate to the field of prosthetic vision.
J Neural Eng 14(5), 2017

We developed pulse2percept, an open-source Python implementation of a computational model that predicts the perceptual experience of retinal prosthesis patients across a wide range of implant configurations. A modular and extensible user interface exposes the different building blocks of the software, making it easy for users to simulate novel implants, stimuli, and retinal models.
SciPy: 81-88, 2017


Selected Guest Lectures

  • BIOEN-460: Neural Engineering
    University of Washington, undergraduate. Lecturers: Prof. Raj Rao, Chet Moritz.
  • PSYCH-508: Core Concepts in Perception
    University of Washington, graduate. Lecturer: Prof. Ione Fine.
  • NRSC-490: Advanced Topics in Neuroscience
    University of Puget Sound, undergraduate. Lecturer: Dr. Alex White.
  • PSYCH-268A: Computational Neuroscience
    University of California, Irvine, undergraduate. Lecturer: Prof. Jeffrey Krichmar.

Certified Software Carpentry Instructor

Teaching Python, shell, Git, and software engineering skills to scientists and engineers (all levels) at bootcamps and in online sessions. Developing new instructional content.
2017 - present

Teaching Assistant

  • CS-143A: Principles of Operating Systems
    University of California, Irvine, undergraduate. Lecturers: Prof. Nalini Venkatasubramanian.
  • CS-171: Introduction to Artificial Intelligence
    University of California, Irvine, undergraduate. Lecturer: Prof. Richard Lathrop.


Grants and Funding

Honors and Awards

  • NIH K99 Pathway to Independence Award: National Eye Institute
    2018 - present
  • Platform Presenter: Eye & Chip World Congress on Artificial Vision
  • Presenter’s Travel Award: Computational & Systems Neuroscience (COSYNE)
  • Innovation in Neuroengineering & Data Science Postdoctoral Fellowship: Gordon & Betty Moore Foundation, Alfred P. Sloan Foundation, Washington Research Foundation
    2016 - 2018
  • GPU Seed Grant: NVIDIA Corporation
  • Best Student Talk Award, IEEE ICRA Neurorobotics Workshop
  • Chair’s Fellowship for Outstanding PhD Applicants: University of California, Irvine
    2012 - 2016