Haptic feedback is a critical feature in VR/AR systems affecting user performance and experience. Previously haptic feedback has been designed via trial and error or in psychophysics experiments. However, this approach faces three issues: (1) it does not adapt to the individual user; (2) psychophysics studies use discrete levels that miss many finer opportunities for improvement; (3) they normally address only a low number of design parameters. The goal of this collaboration is to develop computational approaches to the adaptation of haptic feedback that can significantly improve the usability, expressiveness, and experience of AR/VR technology for individuals. We will study computational methods, especially generative user models and optimization, that can (1) tune the parameters of a haptic system to an individual user, (2) use continuous optimization schemes that exploit the full range of the haptic parameters, and (3) can address a larger number of objectives in a single system.