C-OLiVE stands for Collaborative Orchestrated Learning in Virtual Environments, and is a testbed for assessing the impact of large group collaboration on learning.

Three different selection techniques to perform point cloud annotation tasks: slice, lasso, and bubble 


This project introduces a new approach to increase precision of selection tasks. This approach is based on the use of progressive refinement of the set of selectable objects to reduce the required precision of the task.

The goal of this project is to determine whether a VR application can trigger anxiety in certain individuals. Additional goals include: (1) determining how VR-specific conditions (field of regard and simulation fidelity) compare to known anxiety triggers (i.e. lack of control, uncertainty, and


A suite of novel 3D interaction techniques for analyzing and interacting with 3D volumetric data, and for interaction coarse segmentation of raw volumes.

Should we use more immersive environments for analyzing 3D volumetric data? How could we generalize the effects of immersion for different task types across various scientific domains?