McMahan, Ryan P.

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Ryan P. McMahan is an Associate Professor of Computer Science. In 2016 he won a CAREER award from the National Science Foundation and a Provost’s Award for Faculty Excellence in Undergraduate Research Mentoring. His research interests include:

  • Virtual Reality (VR)
  • Training Transfer
  • 3D User Interfaces (3DUIs)
  • Human-Computer Interaction (HCI)


Recent Submissions

Now showing 1 - 2 of 2
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    Sinus Venosus Defects: Anatomic Variants and Transcatheter Closure Feasibility Using Virtual Reality Planning
    (Elsevier Inc., 2018-12-12) Tandon, A.; Burkhardt, B. E. U.; Batsis, M.; Zellers, T. M.; Velasco Forte, M. N.; Valverde, I.; McMahan, Ryan P.; Guleserian, K. J.; Greil, G. F.; Hussain, T.; 2758150470089704330006 (McMahan, RP); McMahan, Ryan P.
    No abstract available.
  • Item
    VOTE: A Ray-Casting Study of Vote-Oriented Technique Enhancements
    (Academic Press) Moore, Alec G.; Hatch, John G.; Kuehl, Stephen; McMahan, Ryan P.; 2758150470089704330006 (McMahan, RP); Moore, Alec G.; Hatch, John G.; Kuehl, Stephen; McMahan, Ryan P.
    When making selections within 3D user interfaces (3DUIs), a user can fail to select a desired target despite indicating that target during most of the interaction process. This is due to numerous factors that can negatively impact which object is being indicated during the final confirmation step. In this paper, we present a novel vote-oriented technique enhancement (VOTE) for 3D selection that votes for indicated object each interaction frame and then selects the object with the most votes during confirmation. VOTE can be applied to nearly any 3D selection technique, as it does not require additional user input and does not require any prior knowledge of the environment or task. To demonstrate the effectiveness of VOTE, we present a ray-casting selection study that compared traditional, Snap-To, and VOTE ray-casting techniques for a standard multidirectional selection task. The results of our study show that VOTE afforded faster selections than traditional ray-casting and resulted in fewer incorrect selections than the Snap-To enhancement. Additionally, VOTE yielded significantly better effective throughput than traditional ray-casting and the Snap-To enhancement for selections within clustered environments. ©2018 Elsevier Ltd. All Rights Reserved.

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