Contemporary machine learning techniques tend to be resource-intensive, often requiring good quality datasets, expensive hardware, or significant computing power. In a wide array of application domains, ranging from healthcare to mobile computing, these critical resources are lacking. Novel methodologies that enable the optimal utilization of resources can help unlock the full potential of the data science revolution for these domains. Towards this aim, this project will develop theoretically-grounded algorithms to facilitate the design of machine learning models under application-specific resource constraints. The outcomes of the project will help enable machine learning methods to operate with less human-annotated data, less computing power, and on a wider range of hardware platforms. To demonstrate interdisciplinary impact, the resulting algorithms will be employed in the design of efficient hydrological models which aid in predicting and managing water resources. The research will also be strongly coupled with education through the mentoring of undergraduate students, new undergraduate and graduate course development, and live broadcasts of the lectures over publicly accessible online platforms. This project aims to develop the foundational theories and algorithms to guide the efficient use of statistical and computational resources. The research on the statistical front focuses on the data and will uncover the fundamental tradeoffs between the data amount, label quali