My primary research interest is in the computational mechanisms that underly "mid-level" human visual perception, including 2D feature/edge detection and linking, image segmentation, and stereo-depth perception. I believe that a satisfactory description of these mechanisms will come from an interdisciplinary effort across the various sub-fields of neuroscience---such as anatomy, physiology, behavior---and likeliy to beneifit to benefit from useful frameworks applied in other fields---e.g. mathematics, physics, engineering.

My dissertational work investigated the stereo-depth perception in natural images/scenes. When most people observe a scene, they view it in stereo, i.e. via two retinal images, with slightly different vatnage between them. The small difference between these images, or binocular disparity, is a stereo-depth cue that cuse observers to underlying depth strcuture of the observed scene. My dissertational work measured how well people discriminate stereo-depth in natural scenes as a function of both binocular disparity and local varations in depth (disparity-contrast) surrounding target regions. See this publication for more information.

Currently I am a researcher at the eScience Institute at the University of Washington working with Dr. Noah Benson. Here, I am investigating how we might use convolutional neural networks (CNNs) to perform retiontopic mapping based upon MRI data.

As of April 2025, I will be working with Dr. James Elder at York University, investigating the monucular mechanisms for surface attitude estimation.