Artificial Intelligence, Cognition, and Visualization
These projects aim to improve human-machine interaction and to facilitate augmentation of the human mind. We also wish to examine the complex relationships between and networks of existing humans and machines, and improve understanding of AI through visualization.
Memory Augmentation
Huynh, B., Wysopal, A., Ross, V., Orlosky, J., & Höllerer, T. (2022, October). Layerable Apps: Comparing Concurrent and Exclusive Display of Augmented Reality Applications. In 2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (pp. 857-863). IEEE.
Orlosky, J., Toyama, T., Sonntag, D., Kiyokawa, K. Using Eye-gaze and Visualization To Augment Memory: A Framework for Improving Context Recognition and Recall. In the Proceedings of The 16th International Conference on Human-Computer Interaction (DAPI), LNCS 8530, pp. 282--291. Springer International Publishing Switzerland (2014).
Orlosky, J., Huynh, B., & Hollerer, T. (2019, December). Using eye tracked virtual reality to classify understanding of vocabulary in recall tasks. In 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) (pp. 66-667). IEEE Computer Society.
Cognitive Monitoring
Erb, L., & Orlosky, J. (2024, March). Tremor Stabilization for Sculpting Assistance in Virtual Reality. In 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) (pp. 753-754).
Toyama, T., Orlosky, J., Sonntag, D., & Kiyokawa, K. (2015, March). Attention Engagement and Cognitive State Analysis for Augmented Reality Text Display Functions. In Proceedings of the 20th International Conference on Intelligent User Interfaces (pp. 322-332). ACM.
Sonntag, D., Orlosky, J., Weber, M., Gu, Y., Sosnovsky, S., Toyama, T., & Toosi, E. N. (2015, June). Cognitive Monitoring via Eye Tracking in Virtual Reality Pedestrian Environments. In Proceedings of the 4th International Symposium on Pervasive Displays (pp. 269-270). ACM.
Intelligent Visualization
Andrews, K., Benson, J., & Orlosky, J. (2024, March). Alleviating the Uncanny Valley Problem in Facial Model Mapping Using Direct Texture Transfer. In 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) (pp. 751-752). IEEE.