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 Fall 2022, TechArtICT installed Dreamgrass at Exploration Place. The installation was presented as part of the Tallgrass Film Festival. Dreamgrass described a dystopian world where a corporate conglomerate called Happy Corp proudly demonstrated its aggressive use of 80s technology to pacify children. Dreamgrass featured Wichita artists Ernie Kind, Tom McGuire, Kyra Rubinstein, Pankaja Withanachchi,…
Touch #2 is a playful virtual environment and an interactive, musical instrument. Viewers become participants through play. The work transforms any flat wall into a touch-sensitive surface. A projector and infrared camera mount on the ceiling and infrared emitters mount on top of the wall, allowing the work to detect as many as 10 touches…
In 2018 I collaborated with Artist Lisa Rundstrom and artist/engineer Tom McGuire to create the public art sculpture, Avian Migration at Wichita’s new Advanced Learning Library. Avian Migration consists of more than 1,300 LEDs controlled by 8 motion-activated sensors spread throughout the library. I was primarily responsible for the development and implementation of the software…
SoundBlocks is a tangible environment where youth connect blocks to describe network dataflow. The environment explores digital sound manipulation as a personal, meaningful and fun artistic endeavor, rather than as a venture into mathematical, electronic or networking relationships.
SoundScratch is a set of extensions I wrote to manipulate audio in a children’s programming language called Scratch. The environment emphasizes the expressive capabilities of sound through the act of creation and design.
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.