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User Interfaces for today's software are usually created in a one-size-fits-all manner, making implicit assumptions about the needs, abilities, and preferences of the "average user" and the characteristics of the "average device." I argue that personalized user interfaces, which are adapted to a person's devices, tasks, preferences, and abilities, can improve user satisfaction and performance. I have developed three systems: (1) SUPPLE, which uses decision-theoretic optimization to automatically generate user interfaces adapted to a person's device and long-term usage; (2) ARNAULD, which allows optimization-based systems to be adapted to users' preferences; and (3) ABILITY MODELER and an extension of SUPPLE that first performs a one-time assessment of a person's motor abilities and then automatically generates user interfaces predicted to be the fastest to use for that user. My experiments show that these automatically generated, personalized user interfaces significantly improve speed, accuracy, and satisfaction for users with motor impairments compared to manufacturers' default interfaces. 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.