I love art. I believe that practicing art makes for a better data science practice. But mostly I love it because it’s art: it’s beautiful, rewarding, expressive, and fun. I have deep connections to art: I studied design for most of my undergraduate career; I’ve been involved with modern dance for years; I love poetry; I’m a regular patron of the opera; I play the saxophone – of which Gioachino Rossini exclaimed “This is the most beautiful sound I have ever heard!” – and I have a long-term plan for computer-assisted composition and live looping in the indie classical genre.

But it was only a few years ago, when I was on a sabbatical to learn more about data visualization, that I (re)discovered the connection between my data work and my aesthetic interests. I learned about “creative coding” and how to use visualization tools – Processing, in particular – to create art. I also learned how to use Max/MSP/Jitter to create music and, at the same time, capture, analyze, and transform video. These developments led to a surprising turn for a data person: showings in two galleries, a commission for a modern dance performance – in partnership with choreographer Jacque Bell (and my wife) – and two years at Utah Valley University working with a Presidential Fellowship to devise methods for live video looping in modern dance. And, through it all, I have maintained an abiding interest in using the lessons learned to enrich my data practice and teaching.