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HCII at UIST 2016

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UIST 2016

The Human-Computer Interaction Institute had a strong showing at this year's ACM Symposium on User Interface Software and Technology (UIST.) Researchers from the HCII represented nine accepted papers, a Best Paper Award and a Best Talk Award. Congratulations to our students, faculty and their collaborators on their excellent work!

Nine Accepted Papers

The titles and demos for all nine accepted papers are below. Click the titles to launch their demo videos.

  1. A 3D Printer for Interactive Electromagnetic Devices: Huaishu Peng, François Guimbretière, James McCann, Scott Hudson
  2. Advancing Hand Gesture Recognition with High Resolution Electrical Impedance Tomography: Yang Zhang, Robert Xiao, Chris Harrison
  3. AuraSense: Enabling Expressive Around-Smartwatch Interactions with Electric Field Sensing: Junhan Zhou, Yang Zhang, Gierad Laput, Chris Harrison
  4. Bootstrapping User-Defined Body Tapping Recognition with Offline-Learned Probabilistic Representation: Xiang 'Anthony' Chen, Yang Li
  5. IdeaHound: improving large-scale collaborative ideation with crowd-powered real-time semantic modeling: Pao Siangliulue, Joel Chan, Steven P Dow, Krzysztof Z Gajos
  6. Reprise: A Design Tool for Specifying, Generating, and Customizing 3D Printable Adaptations on Everyday Objects: Xiang 'Anthony' Chen, Jeeeun Kim, Jennifer Mankoff, Tovi Grossman, Stelian Coros, Scott E Hudson
  7. Supporting Mobile Sensemaking Through Intentionally Uncertain Highlighting: Joseph Chee Chang, Nathan Hahn, Aniket Kittur
  8. *ViBand: High-Fidelity Bio-Acoustic Sensing Using Commodity Smartwatch Accelerometers: Gierad Laput, Robert Xiao, Chris Harrison
  9. VizLens: A Robust and Interactive Screen Reader for Interfaces in the Real World: Anhong Guo, Xiang 'Anthony' Chen, Haoran Qi, Samuel Christopher White, Suman Ghosh, Chieko Asakawa, Jeffrey Bigham

Best Paper Award & Best Talk Award*:

Gierad Laput, Robert Xiao and Chris Harrison also took home a Best Talk Award and a Best Paper Award for ViBand: High-Fidelity Bio-Acoustic Sensing Using Commodity Smartwatch Accelerometers.

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