My research interests have evolved over the years from studying stereoscopic vision, to natural scene statistics related to binocular vision, to the processing of motion through depth (i.e. 3D motion). This last topic is particularly important because while almost all natural motion is 3D motion, almost all neuroscience on the study of motion processing in the brain had been done using flat 2D motion, because that is easily presented on a computer monitor. This work lead to a drastic rethinking of how neurons in motion sensitive areas of the brain actually encode the 3D direction of motion, and how the code is read out to produce behavior. Finally, I developed a continuous tracking technique to make measurements about the visual system that has many advantages and very few drawbacks relative to tradition "forced choice" psychophysical methods, which are both tedious and time consuming.

In the later years of my career, I have been focusing more on undergraduate education in data science. In this day and age, data science drives almost every decision in business, sports, public policy, etc. And it is vital that  college graduates be at least "data literate" regardless of the major or future plans.