My research interest is to understand and build energy-efficient mobile systems, especially architecture and operating systems support for a future of efficient personal computing. Over the past five years, I have been driving toward a vision of continuous mobile vision services, which invoke frequent vision sensing, compu-tation, and offload to understand a user’s real-world environment, providing vision-based services that relieve users ’ memory and attention. Continuous mobile vision will revolutionize personal computing: immersive interaction will empower consumer applications; sight assistance will aid the memory- and vision-impaired; hazard detection will alert military units; and visual localization will guide robotic drones. However, systems hosting continuous mobile vision face a fundamental challenge: energy efficiency. De-spite recent advances in vision algorithms, current systems are severely limited by the substantial energy con-sumption of always-on vision sensing and processing. Conventional vision systems drain a small wearable battery in 40 minutes and raise the device’s surface temperature to over 55 ◦C [APSys ’14]. This efficiency challenge is symptomatic of a key fact: conventional mobile systems are provisioned for on-demand user interaction, not for continuous service. Systems must be redesigned at all levels to meet the demands of periodically and/or sporadically interpreting visual information. My research takes on this chal-lenge by innovating mobile system support, using an experimental, prototype-driven approach that combines domain knowledge from software systems, hardware architecture, and machine learning. Dissertation Work: Provisioning Mobile Systems for Continuous Mobile Vision My dissertation research ventures through multiple levels of the vision system stack, designing solutions in: (i) application support, (ii) operating systems, and (iii) sensor hardware, as shown in the figure. The principal objective has been to enable energy-proportionality; energy consumption should be proportional to the quantity and quality of the capture and compute needed to complete a set of tasks.