Digital infrastructure is critical to modern society, and a great deal of work goes into ensuring the smooth and continuous operation of systems and networks. This essential labor is carried out by system and network operators. Yet, their work often remains invisible and undervalued—especially when everything appears to function as expected. System operators engage not only in a wide range of technical tasks but also in social and organizational work, such as coordinating with colleagues and helping system users. Their everyday practices directly shape the security posture of their organizations. However, when failures occur, system administrators are frequently blamed for misconfiguration or other types of "human error". Decades of research in human factors demonstrate that focusing on human error alone is insufficient to improve operational security. Instead, it diverts attention from the sociotechnical complexities of these environments and from supporting people in doing their work effectively. This talk will highlight the human dimensions of system operations by drawing on historical perspectives and emphasizing the sociotechnical factors essential to sustaining and securing digital infrastructure.

Block-based visual programming initiatives such as Hour of Code by code.org and Intro to Programming with Karel by CodeHS.com, have transformed introductory computer science education by making programming more accessible to K-8 learners. Despite their accessibility, students often struggle with multi-step reasoning and conceptual abstraction when solving open-ended tasks. Quizzes (such as fill-in-the-gap exercises, and multiple-choice conceptual questions based on code debugging and task design) offer interactive practice and targeted feedback that can promote active learning and scaffold novice programmers. However, manually designing such quizzes is time-consuming and difficult to scale. This thesis tackles these challenges by developing automated synthesis techniques for programming tasks and quizzes, and evaluates their pedagogical utility.

The first part of the thesis introduces algorithmic methods for synthesizing programming tasks and quizzes in block-based environments. Specifically, we develop methods for the following : (i) synthesizing conceptually similar and yet visually dissimilar write-code tasks; (ii) synthesizing adaptive multiple-choice programming quizzes that address student-specific misconceptions; and (iii) synthesizing scaffolded subtasks that break-down complex write-code tasks into simpler subtasks. Each method leverages symbolic execution, sketch-based code mutation, and search-guided generation to ensure pedagogical utility, relevance, and technical correctness. Empirical evaluations conducted through controlled user studies demonstrate the efficacy of these approaches, showing that they not only support novice learners effectively but also outperform existing methods, including next-step code edit based feedback methods.

The second part of the thesis empirically evaluates the pedagogical utility of programming quizzes in these environments via user studies and classroom deployments with K-8 learners. Specifically, we examine: (i) the effectiveness of quiz-based feedback scaffolds with different quiz-types; (ii) the design, validation, and classification of quiz types using cognitive frameworks such as Bloom's Revised Taxonomy; and (iii) the impact of embedding quizzes within programming curricula on post-learning outcomes. Our findings show that quizzes designed using metacognitive strategies and adapted to learners’ attempts significantly enhance engagement and task performance. Moreover, we observe that richer and more diverse quiz types—when integrated into the curriculum— lead to improved post-learning outcomes, while simpler, less cognitively demanding quizzes may hinder post-learning performance.

Overall, this thesis contributes novel synthesis methods for programming quizzes and empirical evidence of their effectiveness in elementary-level programming education. These findings provide a foundation for scalable and adaptive support in elementary computing curricula.

The problem of how to scalably verify large, stateful programs is one of the oldest—and still unsolved—challenges of computer science. Over the last two decades, there has been considerable progress toward this goal with the advent of separation logic, a verification technique for modularly reasoning about stateful programs. While originally only developed for imperative, pointer-manipulating programs, separation logic has in its modern form become an essential tool in the toolbox of the working semanticist for modeling programming languages and verifying programs.

With this thesis, I present a line of work that revisits the foundations of modern separation logic in the context of the separation logic framework Iris. It targets two broader areas: step-indexing and automation. Step-indexing is a powerful technique for modeling many of the advanced, cyclic features of modern languages. Here, Transfinite Iris shows how to generalize step-indexing from proving safety properties to proving liveness properties, and Later Credits enable more flexible proof patterns for step-indexing based on separation logic resources. Automation, on the other hand, is important for reducing the overhead of verification to scale to larger code bases. Here, Quiver introduces a new form of guided specification inference to reduce the specification overhead of separation logic verification, and Daenerys develops new resources in Iris that lay the groundwork for automating parts of Iris proofs using SMT solvers.