In the future, data and algorithms will play an increasingly important role in solving societal-scale problems, from improving people’s living conditions to modernizing the electric grid. Unlike traditional machine learning tasks such as image classification, these problems involve complex physical systems that are safety-critical with humans included, where data could be scarce, corrupted, and even adversarial. Consequently, these systems require robustness guarantees against the uncertainty that arises from (1) anomalous inputs or adversarial attacks, and (2) human interventions; moreover, the algorithms need to be scalable and data-efficient. In this talk I will focus on my recent works in designing computational methods that meet these requirements. The central theme is to develop robust algorithms with formal performance guarantees for complex systems, with emphasis on urban infrastructures that prioritize human values.