Resource usage—the amount of time, memory, and energy a program requires for its execution—is one of the central subjects of computer science. Recently, automatic amortized resource analysis (AARA) has been introduced as a type-based, compositional, and efficient approach for resource analysis. In this talk, I will talk about my research on integrating AARA with the search procedures for (i) compositional worst-case input generation, and (ii) type-directed synthesis with resource-bound guarantees. If time permits, I will also discuss ongoing work for extending AARA with probabilities.

Di Wang is a doctoral student in computer science at Carnegie Mellon University. His research areas are programming languages and software engineering, with a focus on probabilistic programming, type systems, static resource analysis, and program synthesis. Currently, he is working on language-level integrations for Bayesian inference and probabilistic programming systems.