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Stephanie Mueller

December 9, 2015
CSE 1202 on the UCSD Campus



Interacting With Personal Fabrication Machines

Even though they are considered a rapid prototyping tool, 3D printers are very slow. Many objects require several hours of printing time or even have to print overnight. One could argue that the way 3D printers are currently operated is similar to the batch processing of punched cards in the early days of computing: all input parameters are pre-defined in the 3D modeling stage, the 3D printer then simply executes the instructions without human intervention. Since batch processing requires carefully thinking ahead, it is limited to expert users who can reason about the consequences of their design decisions.
In the history of computing, moving away from batch processing enabled completely new interaction paradigms: while batch processing required carefully thinking ahead, command line input allowed for tighter feedback loops, and direct manipulation finally even enabled novice users to quickly iterate towards a solution. I believe repeating this evolution for the editing of physical matter will enable novice users to build objects only trained experts can build today.
Progressing towards this goal requires advances in two areas: First, we need new design tools and interaction techniques for physical editing under computer control. Second, we need faster fabrication techniques to change physical matter in real-time after every editing step. In this talk, I present my research towards solving these challenges.


Stefanie Mueller is a PhD student working with Patrick Baudisch in the Human Computer Interaction Lab at Hasso Plattner Institute. In her research she develops new interfaces for personal fabrication tools, such as laser cutters and 3D printers. She has received several CHI Best Paper and Honorable Mention Awards for her work. In addition, Stefanie is a program committee member for the CHI and UIST conferences. She has been an invited speaker at universities and research labs, such as MIT, CMU, Cornell, UW, ETH, Microsoft Research, Disney Research, and Adobe Research.

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