My name is Damon Crockett. I'm a research scientist and software engineer.

The goal of my research is to make things easier to see and understand. My work is aimed primarily at expert users and audiences, and accordingly, I design powerful, flexible tools that presuppose some background knowledge and can take time to learn.

I'm currently lead scientist at the Lens Media Lab at Yale University, where I build tools for analyzing cultural heritage collections. I began my research career at University of California, San Diego, where I completed a Ph.D. in philosophy and cognitive science, with a focus on the representational contents of perceptual experiences.

Some common threads run through my research: The first is that current forms of information visualization do not adequately exploit the high bandwidth and arbitrary mappability of the visual channel. In short, data visualizations should be denser and far more ambitious—they should encourage not just clearer understanding but conceptual growth.

The second is that finding relevant content in a noisy signal requires adopting a succession of different perspectives. In perception, this may involve turning an object over in your hands or changing the ambient illumination; in data science, this means seeing a diversity of views on your data, from the general to the particular.

And finally, bias is important. Perceptual bias turns a noisy, conflated sensory signal into useful representational content. You can't interpret the world without innate biases, and you can't interpret a data visualization without background knowledge. This is of particular importance now that we are designing machines that can see.