A Platform For Automatically Generating Personalized User Interfaces

I am broadly interested in human computer interaction, machine learning and artificial intelligence. My dissertation demonstrates how to automatically generate personalized adaptive user interfaces. My central thesis is that personalized user interfaces, which are adapted to a person’s devices, tasks, preferences and abilities, can improve user satisfaction and performance. Further, I demonstrate that automatic generation of personalized user interfaces is computationally feasible. In my dissertation work, I developed three systems to enable a broad range of personalized adaptive interfaces: SUPPLE, which uses decision-theoretic optimization to automatically generate user interfaces adapted to a person’s device and usage pattern; ARNAULD, which allows optimization-based systems to be adapted to users’ preferences; and SUPPLE++, a system that first performs a one-time assessment of a person’s motor abilities and then automatically generates user interfaces adapted to that user’s abilities. The results of my laboratory experiments show that these automatically-generated, ability-based user interfaces significantly improve speed, accuracy and satisfaction of users with motor impairments compared to manufacturers’ defaults. I also provide the first characterization of the design space of adaptive graphical user interfaces, and demonstrate how such interfaces can significantly improve the quality and efficiency of daily interactions for typical users.