ABOUT
THOMAS KNOEPFFLER
thomknoe@icloud.com

Thomas Knoepffler is a Design Technologist based in Miami, Florida. His work explores the intersection of materials, interactions, and environments using emergent technologies—such as AI, responsive systems, and digital fabrication. His work spans across mediums, ranging from parametric bio-hybrid products to ambient ergonomic devices to 3D procedural world builders.

He holds a MS in Design Technology from Cornell University and a BS in Integrated Design & Media from NYU, as well as professional experience in UI/UX design and product management for early-stage startups. He has mentored aspiring designers and volunteered with local creative communities alike, dedicated to making design matter for the tomorrows to come.

  • Rhino 3D, Grasshopper, Blender
  • Adobe Creative Cloud, Figma
  • 3D Printing, Laser Cutting
  • Arduino, Raspberry Pi
  • HTML, CSS, JavaScript
  • Python, C#


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TEXTURE.JS

TEXTURE.JS

 Cornell University, AAP
 Design Physical Interaction I, Fall 2024
— DESIGN 6397


DESCRIPTION

A human-robot-controlled turntable that transcribes the contours of objects and their surroundings into spirographic illustrations, while also inferencing spatial data into AI-generated poetry.
texture.js explores poetic computation through human-servo collaboration. The system uses a wooden turntable equipped with an external pressure sensor to capture physical contours of objects and convert tactile input into spirographic illustrations. Simultaneously, OpenAI analyzes the sampled data to generate complementary poetry. Users place paper and a marker on the servo-driven arm, sample objects to activate rotation, and later play back the AI-generated poem. This creates a rich, multi-sensory experience that blends physical interaction with digital interpretation.

The design process started with ideation around rhythmic contours and poetic expression. Early prototypes used malleable cardboard for quick experimentation before transitioning to wood, inspired by the material language of lathe machines. An Arduino Uno con trols the servo motor and processes sensor input, feeding the data into OpenAI prompts to create poetry. Iterative testing refined the mapping between pressure input and rotational speed to ensure smooth, continuous spirographic drawings. User trials helped balance responsiveness and control, while material choices and fabrication methods shaped the final form.

Future iterations deepen human-machine collaboration by adding AI-driven control of the servo arm for dynamic co-authored drawing sessions. A reverse playback mode redraws the illustrations while replaying the generated poetry to explore temporal symmetry and repetition. Modular attachments support a wider range of artistic expressions while preserving the core focus on poetic computation.