USC researchers developed an algorithm called Vibrotactile Preference Learning (VPL) to personalize haptic vibration feedback. Led by USC haptics researcher Heather Culbertson, the system efficiently learns an individual’s preferred vibration patterns for producing a desired emotional response. The yearlong project resulted in the paper, “Vibrotactile Preference Learning: Uncertainty-Aware Preference Learning for Personalized Vibration Feedback,” recently accepted to the ACM Conference on User Modeling, Adaptation and Personalization 2026. The study was conducted in collaboration with Erdem Bıyık, USC assistant professor of computer science and electrical and computer engineering. The research addresses tactile subjectivity, where individual differences in physiology (such as skin thickness and density of touch receptors) and psychology significantly influence how people perceive the same haptic signal. While haptic feedback is embedded in technologies from smartphones to virtual reality, the same vibration pattern can feel very different from one person to another, making predefined haptic libraries often ineffective.