Originally conceived in 2008, Ghost in the Machine (GITM) consists of a webcam and display which mixes and crossfades events in realtime with motion-activated video it has recorded previously. It continually shifts between 3 states: individual, community, and the world.
GITM has been shown in many venues and contexts.
In 2011, as part of Hack.Art.Lab, I collaborated with composer Mary Ellen Childs and percussionist Michael Holland to create live animation triggered by live performance of Mary Ellen Childs’ composition “Still Life.” We analyzed the piece into 11 sections and created algorithmic video triggered by sound and motion to match each of the 11 sections. The video was projected…
I built a connector to access the Gameboy circuitry using a solderless breadboard and used it to interface flash memory and a Digital to Analog Convertor (DAC) to the Gameboy.
Whispering Woodlands was an outdoor installation created by TechArtICT. It was installed at Exploration Place in Wichita, Kansas from November 2023 through January 2024. The work featured 24 independently controlled sets of speakers and LEDs, all synchronized to create an immersive sound and lightscape. Using eclectic audio ranging from thunder and rain to spaceships and…
In 2012 I created my first interactive touch wall: Touch #1. The work built on my experience creating the visuals for Still Life and was largely inspired by seeing autistic children experiencing pure joy while interacting in an immersive environment. Touch #1 received a great response and was later installed at Exploration Place and at…
Auralis is a musical instrument constructed from a multitouch table and custom software ported from my earlier touch wall Touch #2. Merging sound, interactivity, and sequencing, Auralis is simultaneously engaging, meditative and soothing. I created the work for youth ages 8-14. A virtual world of suns, stars and planets interact with each other. Participants run…
I worked with VÃctor Adán to explore how if we might train a machine to recognize chords changes in music. We tried multiple models to solve the problem, including Support Vector Machines, Neural Networks, Hidden Markov Models, and a few variations of Maximum Likelihood systems.